text:
The Abstract Podcast
Shin-kicking, lawn mower racing, and the surprising athleticism of Not Sports
In this episode, we talk about the weirdest competitive sports you've never heard of.
Cotswold Olympicks
Inverse Staff
8.7.2020 1:30 PM
Since the dawn of time, people have been coming up with pastimes to stay entertained, inventing sports then reinventing sports throughout history.
There are pro leagues and the Olympics, championship tournaments and little league. And when things get boring, humans get creative — and sports get … weird.
Going toe-to-toe against someone not literal enough for you? Toe wrestling's your game.
Need an extreme sport that blends performance art with a domestic chore? That's what extreme ironing is for.
Would boxing make chess more exciting? Done. Totally a thing.
There's something for everyone in the world of competitive sports, and as the pursuit of amusement continues to reward us with absurdly entertaining sports, the strangest games become the most weirdly satisfying in ways that many mainstream sports simply can't compare.
In this episode of The Abstract, we talk about the weirdest competitive sports you've never heard of.
Our first story is about a centuries-old sport that requires a spectacular amount of fortitude and athleticism: shin-kicking. A perennial crowd-pleaser at the Costwold Olimpick Games — and as literal as it sounds — competitive shin-kicking has endured for over 400 years by never losing its original focus and embracing a sense of fun.
Our second story is about a form of motorsport where competitors race modified vehicles of the ride-on or self-propelled variety. In short, lawn mower racing. Turning a yard chore into an international sporting event, these competitive races continue to attract all walks of life by capturing the true meaning of sports — blending ambition and expertise with a sense of fun and adventure.
Read the original Inverse stories:
Shin-kicking: A game created to troll 17th-century puritans is still thriving
A veteran lawnmower racer on what it takes to zip at 60 miles per hour
Subscribe to The Abstract wherever you listen to podcasts: iTunes | Spotify | TuneIn | RadioPublic | Stitcher
Follow Emma Betuel on Twitter
Follow Inverse on Twitter
We're hosted and produced by Tanya Bustos
Right now, facts and science matter more than ever. That's part of the reason for The Abstract, this all-new podcast from the Inverse staff that focuses exclusively on science and innovation. Three new episodes are released a week, and each covers one theme via two related stories. Each features audio of original Inverse reporting, where the facts and context take center stage. It's hosted by the Tanya Bustos of WSJ Podcasts. Because we're Inverse, it's all true but slightly off-kilter. It's made for people who want to know the whole story. —Nick Lucchesi, executive editor, Inverse


text:
Home Tech Google Introduces New Pixel Buds A Wireless Headphones
Google Introduces New Pixel Buds A Wireless Headphones
Google announced this Thursday (3rd) a new pair of wireless headphones from the Pixel Buds line. Images of the accessories have been leaked in recent weeks, as well as some of the model's features.
The Pixel Buds A-Series carry the same technical audio characteristics as the Pixel Buds 2020, plus respond to voice commands with "Ok, Google".
The design difference is light, with a change to the end hooks, which have been made smaller and softer to increase wearing comfort. The Dark Olive color, which keeps the case in shades of white, is another addition. In terms of connectivity, it brings the Bluetooth 5.0 standard.
Google's idea is to position Pixels Buds A-Series as a cheaper and simpler alternative to AirPods, especially in price. To do this, however, some features were cut — including wireless recharging of the box, reduction of wind noise, and gesture control.
The headphones are IPX4 certified for water and sweat resistance, and the battery is capable of lasting five hours of uninterrupted use.
The box is also used for recharging and concentrates more than 24 hours of energy to be transferred to the headphones, with 15 minutes being needed to guarantee three hours of use.
The Pixel Buds A-Series headphones will be released on June 17, 2021, initially only in the US and Canada. In these two countries, online pre-order is already open at a price of US$99 — about R$505 in direct currency conversion.
Rian Johnson's Poker Face Reviews Are Already Online, and Critics Aren't Bluffing About How Great the Mystery Series Is
Manchester United Makes January Transfer U-turn As New Plan to Sign Striker Emerges
Ryan Gosling vs. Andrew Garfield, Who's a Better Kisser? Ellen DeGeneres Once Forced Emma Stone to Choose a Side
Universal Pictures Has Rejected a Biopic About Madonna Starring Julia Garner
Panic! At The Disco Ending After 19 Years – Here Is The Official Statement
Announced Performances at The Grammy Awards in 2023 — Bad Bunny, Lizzo, Sam Smith, Mary J. Blige and Others
Scooter Braun Reportedly Becomes Hybe America's Sole CEO As K-Pop's Global Expansion Continues
Marilyn Manson Settles Esme Bianco's Sexual Assault Lawsuit
US Senator Introduces New Bill to Ban TikTok Across The Country: "it threatens the privacy of our children, as well as their mental health"
The Relationship Between Park Hyun-shik and Jeon So-nee Takes an Unusual Turn For The Better in The Drama "Our Blooming Youth"
Twitter will clear the presidential account of followers
The countdown has begun for the Samsung Exynos 2100!

redpajama_set_name: RedPajamaCommonCrawl


Find Physics tutors in Corringham with Home Tutors Directory. We also list a range of tutoring agencies and tuition centres who can provide Physics teachers in Corringham for primary, GCSE, A-Level and University courses. It is free to contact the tutors on this page who are offering Physics lessons in Corringham.
Can't find private tuition in Corringham for your subject? You may wish to view our online tuition page. Alternatively use the navigation links to find tutoring in other areas of Essex.


The majority of those in government are lawyers. Presently the entire body of representatives of one of the political parties is lawyers.
It may seem that because government is about laws that lawyers would be most suited for the job. But that must not be. Government continues to run afoul and over 80% of citizens don't trust it or those who are swimming in its swamp.
Rather than fealty to the Constitution and freedom, politicians tut-tut that and press forward with their personal ideologies, self-serving agendas, and malversation using back-room deals, lies, bribes, plea bargains, compromises, injustices…all the things we find despicable that also can occur in the judicial system.
The fault lies with the way lawyers are trained and the legal system is designed. Doing what is right and seeking justice may be the reason people become lawyers, but that is not the way the law is practiced. Changing the world for the better may be the reason lawyers go into politics, but that is not how politics is practiced.
What happens to those wonderful ideals?
Conscience is surrendered to groupthink. A law student is trained to relinquish individual conscience and sense of ethic and justice to obedience to the legal system. The problem is that the legal system is a human concoction of words fraught with imperfections and double meanings creating loopholes that permit injustices to leak through. For example, the legal system says everyone is entitled to aggressive representation. Thus there is the practice by defense attorneys to do all that is possible to get people off, guilty or not. There is also the practice by prosecutors of doing everything possible to put people in jail. Setting a serial murderer free or putting an innocent person behind bars is unconscionable to anyone with a pulse. But lawyers who do such things sleep at night because they were obedient to the system... "I just was doing what I was told (in law school)."
Once in the grip of the political system where so much power and money circulates, lawyers trained to work a system, work the political system for power, personal interests, and ideological ends. With silky words many politicians cast a moral cloak over their unconstitutional and selfish base motives. They manipulate our freedoms like they defend guilty parties or prosecute innocent ones. Government has become like one great big wheeling-dealing, free for all court room.
The problem is not lawyers per se. It is the human proclivity to surrender conscience to others in the form of their institutions. Exercising individual conscience is perhaps the hardest thing we attempt to do as humans. It is too exacting, uncompromising, and usually does not bring us immediate benefit. So we do the lazy thing and turn conscience over to others, particularly if their conscience grants us ease, comfort, and other benefits.
Lawyers are not alone. The call of conscience to ease pain and suffering may be the reason people become doctors. But medical school trains that out and replaces it with obedience to the official standard of care. If giving an eighty-five-year-old seventeen pharmaceuticals is not forbidden, then it can be done in "good conscience" even if it results in the death of the patient. (As occurred with both my mother and sister.) Modern medicine is responsible for more deaths than any other cause. The Greatest Threat To Health, yet we hear not a whisper about it because doctors are obediently following their training and the system.
The same can be said of those who surrender their conscience to a religious organization or holy book. History is bathed in the blood of millions because people have replaced their own conscience with that of other humans in the form of their authoritarian institutions.
Such allegiance is not a very smart thing for any smart person to do, but it is particularly dangerous in government because of the power it can exert. The only way to rescue the country from the undertow of a government embedded with those who are committed to something other than truth, freedom, and justice, is to elect representatives who are philosophically deep, historically alert, and have demonstrated such savvy by their actions. They should be measured by what they have done, not their looks or what they say and promise. They should be permitted in office for only a short stint so they can focus on doing what is right, rather than posture and manipulate for re-election. Then they must go back to society with the rest of us to live under the system they create.


text:
Attorney General Cameron joins amicus briefs supporting four states in banning elective abortions during COVID-19 pandemic
(WBKO)
By WBKO Staff Writer
Published: Apr. 2, 2020 at 1:48 PM CDT
Attorney General Daniel Cameron today announced Kentucky joined amicus briefs supporting Texas, Ohio, Alabama, and Oklahoma in their efforts to slow the spread of the coronavirus (COVID-19) and conserve personal protective equipment (PPE) by halting elective medical procedures, including abortions.
The amicus briefs support each of the state's executive orders, which require abortion providers to follow the same mandates as all other medical providers and stop elective procedures during the COVID-19 pandemic.
Abortion providers are suing the states, claiming they are entitled to receive a blanket exemption from such orders and should not have to follow the same rules as everyone else.
The United States Court of Appeals for the Fifth Circuit this week temporarily upheld the Texas executive order halting elective procedures, staying a decision by the lower court that had allowed elective abortion procedures to continue.
Attorney General Cameron has joined two amicus briefs in the Fifth Circuit on this issue, one arguing in favor of the temporary ruling, and one arguing that the Texas executive order should be upheld for the duration of the pandemic.
"Abortion advocates have long said that they are simply 'pro-choice,' which refutes their argument that abortions are anything other than elective procedures," said Attorney General Cameron. "By ignoring the ban on elective medical procedures, abortion providers are willfully putting their own patients at risk for contracting COVID-19 as well as risking the health of our frontline healthcare workers who need the PPE used for elective abortion procedures. We stand with Texas, Ohio, Alabama, and Oklahoma in their efforts to slow the spread of COVID-19, and we, again, call on our Acting Secretary for the Cabinet for Health and Family Services, Eric Friedlander, to certify that the Commonwealth's abortion providers are violating his ban on elective medical procedures so that our office can take immediate action here in Kentucky."
Last week, Attorney General Cameron called on Acting Secretary Friedlander to certify, pursuant to KRS 15.241, that Kentucky's abortion providers are violating the ban on elective medical procedures during the COVID-19 pandemic.
Secretary Friedlander has not responded to the request, and abortion providers continue to perform elective abortion procedures in the Commonwealth, in violation of Gov. Beshear's executive order.
On March 14, Gov. Beshear recommended that hospitals cease performing elective procedures. Subsequently, on March 23, Acting Secretary Friedlander ordered all "non-emergent, non-urgent in-person medical, surgical, dental, and any other healthcare practice or procedure" to cease. As explained by Acting Secretary Friedlander, the outbreak of COVID-19 is "a public health emergency." Therefore, "[a]ggressive social distancing measures have been mandated by emergency order as a necessary measure to limit and contain the spread of the COVID-19 infection."


Illinois' abundant water resources face a number of infrastructure planning challenges, including storm-water runoff, increasing residential, industrial and commercial use, and impacts from climate change.
Urban storm-water runoff is one of the major sources of impairment to Illinois' lakes and streams. Impervious surfaces (roads, rooftops) greatly increase the volume and velocity of storm-water runoff, which picks up pollutants that can degrade water quality if discharged untreated into lakes or streams.
Urban runoff pollutants include: sediment; oil, grease, and toxics from vehicles; pesticides and nutrients from lawns and gardens; viruses, bacteria, and nutrients from pet waste and septic systems; road salts; and heavy metals from roof shingles, motor vehicles, and other sources. In periods of rainfall or snow melt, the waste-water volume in a combined sewer system can exceed the capacity of the system; in which case, the system is designed to overflow and discharge excess waste-water directly to nearby streams, rivers, lakes, or estuaries. Polluted runoff and combined sewer overflows can harm people recreating on affected waterways, kill aquatic life and contaminate important sources of drinking water.
Illinois residents and businesses use a great deal of our water supply on a daily basis. Illinois industry, farms, businesses, and households use 21 billion gallons of water a day. Roughly 20 billion gallons a day are withdrawn from surface waters and another billion gallons per day are withdrawn from groundwater. Geographically, the overwhelming amount of water withdrawn is used in northern Illinois, where most of the population, business, and industry are located. Half of the water withdrawals in Illinois go towards thermoelectric power generation.
The challenges facing the state's water resources are further complicated by the impacts of climate change. As the climate continues to warm, precipitation patterns change and in recent decades, the number of extreme rainfall events has increased dramatically. These type of precipitation events lead to excessive runoff that transports pollution and contributes to localized flooding. Statewide, the annual number of precipitation events greater than 3 inches has increased by 83% over the last 50 years, and the amount of total precipitation during these events has increased by 100%. As the climate continues to warm, the number of days with rainfall greater than 1 inch is projected to increase up to 30% by mid-century.
While major floods on the Mississippi and Illinois Rivers attract the national spotlight, floods are common in Chicago and its suburbs. Cook County has had 11 federally declared emergencies due to flooding since 1991. In those heavily urbanized areas, heavy rains overwhelm storm-water systems, causing localized flooding. Often, this flooding could be prevented with proper infrastructure planning and implementation.
The frequency of larger storms that cause this kind of flooding will increase, according to the Chicago Metropolitan Agency for Planning. Chicago can expect today's "10-year storm" (4.47 inches of rain in 24 hours) to occur every five years by the end of the century and today's "100-year storm" (7.58 inches of rain in 24 hours) will become the 50-year storm. It's worth noting that Chicago has already experienced two 10-year storms in the last four years and three 100-year storms since 1980.
For the Chicago area, heavy rains of this magnitude also mean trouble for Lake Michigan and the millions of people who use its beaches and rely on it for drinking water. Heavy rains combined with low water levels on Lake Michigan causes the Chicago River to "re-reverse" itself and flow back into the lake (as it did on June 16, 2015), carrying millions of gallons of untreated sewage with it.
One solution to help Illinois address water quality and supply concerns, as well as help communities better manage the changes caused by climate change, is the use of green storm-water infrastructure, which uses vegetation, soils, and natural processes to manage water and create healthier urban environments. Green infrastructure may refer to natural areas that provide habitat, flood protection, cleaner air, and cleaner water, or to storm-water management systems that mimic nature by soaking up and storing water.
FACT: Illinois withdraws more water for its nuclear- and coal-powered plants than any state in the U.S., except for California.
Created by the Illinois River Restoration Act of 1997 to coordinate initiatives, projects, and funding to promote the ecological health of the Illinois River and its tributaries by addressing issues identified in the Integrated Management Plan for the Illinois River Watershed.
Chaired by the Lt. Governor and composed of a diverse group of citizens, not-for-profit organizations, and state and federal agencies.
Created by the Mississippi River Coordinating Council Act in 2010, the Council is modeled after the Illinois River Coordinating Council and is charged with coordinating policies and programs promoting the intertwined environmental and economic health of the Mississippi River and its tributaries within the State of Illinois.
Chaired by the Lt. Governor and iscomposed of a diverse group of citizens, not-for-profit organizations, and state and federal agencies.
The Middle Fork River is Illinois' first State Scenic River, designated in 1986 by Governor Thompson. In 1989 U.S. Secretary of the Interior Lujan designated the Middle Fork as a National Scenic River. The Middle Fork is the first river in Illinois to be included in the National Wild Scenic Rivers System and is protected by state and federal law.
Provide permanent protection for a 17-mile segment of the river in Vermilion County, including conservation easements on both sides of the river. Most of the area along the river is forested, and there are also several prairie sites.
The Middle Fork River Valley supports a great diversity of plants and animals including 57 types of fish, 45 different mammals, and 190 kinds of birds. Of this diverse wildlife, there are 24 species officially identified as State threatened or endangered species. The Middle Fork River valley also includes unusual geologic formations, various historic sites, and over 8,400 acres of public parks.
In 2005, the Great Lakes Governors and the Premiers of Ontario and Quebec signed the Great Lakes-St. Lawrence River Basin Sustainable Water Resources Agreement. At the same time, the Governors endorsed the companion Great Lakes-St. Lawrence River Basin Water Resources Compact, which became law in the United States in 2008. The Compact is legally binding among the eight Great Lakes states and the federal government, mandating the states to jointly determine how to manage the waters of the Great Lakes Basin.
Since its inception, the state's Clean Water and Drinking Water SRF has disbursed more than $3 billion in low interest loans to municipalities to help improve their water infrastructure. This financial assistance has been critical to helping communities construct and improve wastewater and drinking water treatment systems.
But financing from the SRF has not been available for communities to deal with the growing challenge of stormwater management, to implement innovative green infrastructure solutions, or increase their ability to deal with the increasing future risks of floods and droughts. Legislation passed in 2014 needs to be implemented by the Illinois EPA to allow SRF support to be available for these purposes.
Helps local governments and other organizations fund the implementation of green infrastructure best management practices. In 2013, the General Assembly passed two bills designed to give municipalities and counties more tools and authority to increase the use of green infrastructure in stormwater management. So far, the IGIG has awarded over $14 million for 35 projects, 15 of which are complete. The completed projects have: reduced nitrogen load by 960 lbs/yr; reduced phosphorus by 276 lbs/yr; reduced suspended solids by 62,113 lbs/yr; and reduced sediment load by 223 tons/yr.
Note: IGIG is in a state of change and at this time, no new applications are being accepted.
The Wildlife Action Plan ensures that our state's biodiversity and abundance of wildlife is maintained for the long-term by focusing on habitat. The Green Cities campaign part of the plan looks to improve community planning efforts by including open space and wildlife needs into those plans. Additionally, the campaign addresses urban area use in migration routes, and promotes habitat protection and restoration whenever possible.
The Act establishes a working group with representatives from state, federal, local agencies, and other interested parties to review and evaluate the latest research, policies, and procedures regarding urban flooding. Urban flooding can happen anywhere, not just in floodplains. This Act helps Illinois communities better understand urban flooding and identify innovative stormwater solutions to protect homes and the environment.
In furtherance of safeguarding the health and well-being of the populace, this Act requires any community water supply system to designate an operator who will be directly responsible for that system's water supply and distribution. It also requires operators to be properly certified with the skills necessary to operate the community water supply.
Illinois should engage in long term planning to protect water supply and quality that emphasizes water conservation.
Protecting and restoring our waters for people and wildlife requires an integrated approach to water resource management.
The health of our rivers and the integrity of our drinking water are too entwined to silo our water quality and water supply solutions. Regulations and programs must both incentivize and require actions that consciously recognize these connections. For instance, the Illinois State Revolving Loan programs can prioritize low-interest loans and offer principal forgiveness for innovative solutions, such as green infrastructure and advanced treatment, to reduce flooding, improve water quality, and protect our public water supplies. Water supply permits and programs should consider conservation measures, like the systemic use of green infrastructure, that improve the quality of our rivers and lakes. Our investments and regulatory requirements should reflect long-term plans and commitments to improve and restore our water resources as natural assets, economic drivers, and community centers.
Illinois should ensure its regulatory requirements fully enable the use of green infrastructure to help communities better manage storm-water.
Green infrastructure solutions reduce the risk of flooding by intercepting storm-water before it gets into the sewers, capturing it in swales, buffer strips, porous pavement, and other natural or engineered structures that allow storm-water to soak into the ground or be retained for a short period of time. Green infrastructure solutions can dramatically reduce the amount of storm-water that runs off a specific site. If implemented at scale across a community, green infrastructure can diminish the risk of urban flooding by reducing the risk of storm-water systems being overwhelmed by intense rainfall events. Programs that leverage agency support and engage communities to systemically build and maintain green infrastructure hubs, such as innovative school and community gardens, can both dramatically reduce localized flooding and cultivate a water ethic to ultimately transform how people in each affected area see, value and manage water.
Illinois should upgrade its aging water infrastructure and fix leaks in the system.
IEPA has convened a water loss accounting advisory committee and trained hundreds of utilities across the state on the basics of water loss accounting. The State should continue this effort, providing technical assistance funding to utilities that have completed the training and have identified necessary infrastructure to cost-effectively repair leaks.
Illinois should assign State Revolving Funds (SRF) to projects that result in decreased water usage, decreased discharges of water pollution, increased use of green infrastructure, and increased community resilience.
More on sewage overflows into the Great Lakes by the Alliance for the Great Lakes.
Prairie Rivers Network on preventing stormwater runoff.
Friends of the Chicago River on threats to the Chicago River.
More on the importance of stormwater management by the Chicago Metropolitan Agency for Planning's (CMAP).


M & J Construction Co., Inc.
Specializing in windows, doors, curtain walls, and storefronts to meet the needs of residential and commercial construction since 1955.
Licht Properties division of Jacob Licht, Inc.
E. F. O'Donnell & Sons Co., Inc.
John F. White & Co. Inc.
At Mott & Chace Sotheby's International Realty we believe that truly extraordinary homes are different at every level and they require real estate services that are as well.
Apollo Roofing & Sheet Metal, LLC.
E.W. Audet & Sons Inc.


text:
Bafomet (Baphomet) este un presupus idol al cărui nume a apărut pentru prima oară în stenogramele proceselor templierilor la începutul secolului al XIV-lea. Unii istorici moderni consideră că numele este o scriere greșită, în franceza veche, a numelui Mahomet (Mahomed).

În secolul al XIX-lea, numele a fost popularizat datorită unor scrieri pseudoistorice care au încercat să lege cavalerii templieri de unele teorii ale conspirației, discutând despre distrugerea ordinului lor. Numele Bafomet (Baphomet) a devenit atunci asociat cu o imagine de "capră sălbatică" desenată de Eliphas Lévi.

Ocultistul francez Eliphas Lévi a susținut că numele a fost derivat din codarea cabalistică:
Numele lui Baphomet al Cavalerilor, care ar trebui pronunțat cabalistic invers, este compus din trei abrevieri în limba latină: Tem. ohp. AB., Templi omnium hominum pacts abbas'', "tatăl templului păcii al tuturor oamenilor".
! Sathe Cvintero Dueccum Aeriventus !

Note

Referințe
 Malcolm Barber, "The New Knighthood: A History of the Order of the Temple", Cambridge, 1994

Demoni

redpajama_set_name: RedPajamaWikipedia


5 December: Major Malaysian utility Tenaga Nasional Bhd (TNB) has completed a 50MW(AC) solar power project in Selangor, Peninsula Malaysia, the largest project in the country.
The project, won in the government-led Large Scale Solar (LSS) auction by TNB's subsidiary, TNB Sepang Solar Sdn Bhd, is located at Mukim Tanjung 12, Kuala Langat, south of the capital Kuala Lumpur. The first phase of the project started supplying power to the grid in October.
Malaysia is due to announce details of the 500MW LSS 3 tender in January.
4 December: The EBRD will provide a US$265 million loan to Jordan's National Electric Power Company (NEPCO) to strengthen NEPCO's balance sheet through refinancing of existing short-term debt and to fund vitally needed investments to enhance the capability of the grid to absorb and manage renewable power.
Alongside the loan the Jordanian government, NEPCO and the bank have agreed on an ambitious programme of reforms that will strengthen NEPCO's governance and operations. This will be supported by grant-funded technical cooperation assignments.
Construction of new substations, automated grid management systems and transmission infrastructure will improve the integration of green energy into the national electricity network. As a result, solar energy generated in the north and south of Jordan will be able to feed into the grid and be directed towards areas with high demand, such as the capital Amman.
30 November: Canada is to invest CAD$2 million in Sky Solar Canada for a total of 12 solar-powered electric vehicle charging stations at five sites across Ontario. Three of the sites are located in Cobourg, one in Markham and one in Oxford Station.
Many of the charging stations are bi-directional, which means they will also be able to deliver energy from the battery to the electricity grid. The demonstration project will help identify the economic benefits of bi-directional charging.
The funding is part of Canada's CAD$182.5 million investment to grow Canada's electric vehicle infrastructure.


The Hearing Center of Golden Valley is well known in the community for the safest and most advanced equipment and technology available today for the hearing impaired. With well over 60 years combined experience our highly skilled and trained staff is here to serve all your hearing enhancement needs. We offer the latest technology available in the industry; while maintaining the most user-friendly and stylish instruments at unbelievable savings to you.
Our promise to you. We promise to provide you with outstanding customer care and state-of-the-art hearing instruments to help you hear better. Satisfaction with your new hearing instruments is our top priority. We will guide you through the important steps to improve your hearing and get you back to your daily life. Stop in or call the office to schedule your free evaluation and meet our friendly and courteous staff. We will assist you and answer all your questions. We offer home visits to the surrounding areas including Golden Valley, MN; St Louis Park, MN; Plymouth, MN, and Crystal, MN!


We deliver the speed, efficiency and scale required to power your data in real time. Consolidate all types of data—big, fast, random, sequential—on the IntelliFlash Data Storage Platform and make it come alive.
Get a deep dive into the architecture and operating system used by all IntelliFlash storage arrays.


text:
\section{Appendix}

\begin{table*}[t]
\centering
\setlength{\tabcolsep}{3pt}
\scalebox{0.9}{
\begin{tabular}{p{15cm}}
\toprule
\textbf{Topic:} How to Reduce the Redness of Sunburn: Healing and Concealing Sunburns. \\
\toprule
\textbf{Article:} Try to drink at least 10 full glasses of water each day for a week after your sunburn ... This is the traditional go-to remedy when dealing with a burn. The gel of the aloe vera plant has natural anti-inflammatory properties and can speed up the healing process if applied correctly ... Get out a small bowl and mix equal parts baking soda and cornstarch ... You can use the leaves and bark of the witch hazel plant for medicinal purposes ... You can fill up a bottle and spray the vinegar directly on your skin for relief ... Many natural healers swear that potatoes can reduce pain and inflammation. Get a few potatoes and use a knife to cut them into thin slices ... This one is a bit of a long-shot but, if nothing else, the cool temperature of the yogurt may soothe your skin ... Light, cotton garments that fall away from the skin are your best options during your recovery period ... Apply a green-tinted primer to the burned areas to counterbalance the appearance of redness  ...
  \\
\toprule
\textbf{Reference:} Drink a lot of water. Apply aloe vera. Create a baking soda paste. Use witch hazel. Apply apple cider vinegar to the area. Apply potato slices to the area. Apply live cultured yogurt. Wear loose and dark clothing. Use make-up to cover the redness.
 \\
\toprule
\textbf{Trans-Sum:} Drink plenty of water. Apply aloe vera gel to the skin. Make a baking salt and corn flour mask. Use hazelnut extract. Apply apple cider vinegar. Use potatoes. Apply yogurt to the skin. Apply blush.
 \\
\toprule
\textbf{Trans-Sum-R:} Drink plenty of water. Apply aloe vera gel. Use baking salt and cornmeal. Use hazelnuts and bark. Apply apple cider vinegar. Apply potatoes. Apply yogurt to the skin. Avoid wearing makeup.
 \\
\toprule
\textbf{DC+Synth+MT (Ours):}  Drink plenty of water. Apply aloe vera gel to the burn. Mix baking soda and cornstarch. Use witch hazel. Apply apple cider vinegar to the burn. Use potato slices. Apply yogurt to the burn. Wear dark clothing.
 \\
\toprule
\end{tabular}
}
\caption{An example output summary for Trans-Sum, Trans-Sum-R and DC+Synth+MT. Human annotators preferred the output from DC+Synth+MT.}
\label{tab:example_appendix_1}
\end{table*}

\begin{table*}[t]
\centering
\setlength{\tabcolsep}{3pt}
\scalebox{0.9}{
\begin{tabular}{p{15cm}}
\toprule
\textbf{Topic:} How to Speak Portuguese: Studying Basic Portuguese Vocabulary. \\
\toprule
\textbf{Article:}   Practice saying ordinary phrases so you can carry on a casual conversation in any situation ... Practice polite phrases like ``please'' and ``thank you''. Learn a few ways to communicate your understanding or confusion ... If you will be traveling in Portugal, learn some of the basic questions and statements you will need to get around ...  Learn how to ask for rooms, order off a menu, and talk to shopkeepers ...  Portuguese numbers sometimes reflect the gender of the word they are applied to. For instance, if you are talking about a woman, you would say ``uma mulher'', but for a man you would say ``um homem.''.
  \\
\toprule
\textbf{Reference:}  Learn a few greetings. Master some basic conversational phrases. Learn key travel phrases. Practice your shopping and restaurant vocabulary. Learn how to count in Portuguese.
 \\
\toprule
\textbf{Trans-Sum:}  Learn some basic phrases to communicate basic conversation. Practice basic phrases to communicate understanding or confusion. Learn some basic questions and statements to communicate when traveling. Learn how to order a room or talk to merchants. Learn how to talk about gender.
 \\
\toprule
\textbf{Trans-Sum-R:} Learn basic conversational phrases. Use simple phrases to communicate understanding or confusion. Learn basic questions and statements when traveling to Portugal. Learn how to order a room, menu, or speak to merchants. Learn how to say "um" or "um homem" if you're talking about a woman.
 \\
\toprule
\textbf{DC+Synth+MT (Ours):} Learn some basic conversational phrases. Learn some polite phrases Learn some basic phrases when communicating in public. Learn some basic phrases when communicating in public. Learn how to communicate with people. Learn how to communicate with numbers.
 \\
\toprule
\end{tabular}
}
\caption{An example output summary for Trans-Sum, Trans-Sum-R and DC+Synth+MT. Human annotators preferred the output from DC+Synth+MT.}
\label{tab:example_appendix_2}
\end{table*}

\begin{table*}[t]
\centering
\setlength{\tabcolsep}{3pt}
\scalebox{0.9}{
\begin{tabular}{p{15cm}}
\toprule
\textbf{Topic:}  How to Teach English As a Second Language to Beginners: Embracing Best Practices. \\
\toprule
\textbf{Article:}  One great way to facilitate learning is to encourage students to avoid speaking languages other than English in the classroom ... When explaining an activity or giving directions about homework, classwork, or a project, you should always give both verbal and written instructions ... This will aid in word association and in pronunciation ... No matter what type of lesson you are teaching or what activity your students are doing, you should monitor them constantly ... Teaching English as a second language to beginners is a lot more effective when you use a variety of types of learning ... When teaching beginners or very young students, break the lesson into several pieces of about 10 minutes.
  \\
\toprule
\textbf{Reference:}   Encourage students to speak only English in the classroom. Provide verbal and written instructions. Monitor students' progress constantly. Promote a diversity of modes of learning. Break lessons into small pieces.
\\
\toprule
\textbf{Trans-Sum:}  Encourage students to speak English. Give both oral and written instructions. Control your students. Encourage different types of learning. Divide lessons into small pieces. Change your lesson types often.
\\
\toprule
\textbf{Trans-Sum-R:} Encourage students to speak English. Provide both oral and written instructions. Monitor your students. Diversify your teaching methods. Divide the lesson into short pieces. Switch up your teaching style.
 \\
\toprule
\textbf{DC+Synth+MT (Ours):} Encourage students to speak English. Give both verbal and written instructions. Check on students regularly. Encourage a variety of learning methods. Break your lessons down into small chunks. Vary your lesson types.
 \\
\toprule
\end{tabular}
}
\caption{An example output summary for Trans-Sum, Trans-Sum-R and DC+Synth+MT. Human annotators preferred the output from Trans-Sum-R and Trans-Sum over DC+Synth+MT.}
\label{tab:example_appendix_3}
\end{table*}

\begin{table*}[t]
\centering
\setlength{\tabcolsep}{3pt}
\scalebox{0.9}{
\begin{tabular}{p{15cm}}
\toprule
\textbf{Topic:} How to Live an Active Life with COPD: Participating in Exercise and Activities with COPD. \\
\toprule
\textbf{Article:}  With a serious lung disease like COPD, you have to be exceptionally careful when you start physical activity. Although exercise can help improve your COPD, you still need to ease into activities slowly ... Increasing your lifestyle activity is a great way to stay active without overdoing it. These are not cardio activities, but they also help keep your body moving and your lungs working ... When you're ready to progress to more structured exercise, you need to plan to include a warm-up. This is an essential component of safe exercise for those with COPD ... Unless cleared by your physician, you should only participate in aerobic activities that are low in intensity. This level is the most safe for patients with COPD ... Aerobic exercises are great to help improve the condition of your lungs and improve your cardiovascular system; however, strength training is an essential form of exercise as well.
  \\
\toprule
\textbf{Reference:}  Ease into activities. Increase your lifestyle activity. Always do a warm-up. Add in low-intensity cardio exercises. Do light strength training. Try pilates and yoga for breathing exercises.
\\
\toprule
\textbf{Trans-Sum:} Start slowly. Include daily activities. Include a warm-up. Perform low-intensity aerobic exercises. Perform strength training. Do yoga or pilates.
\\
\toprule
\textbf{Trans-Sum-R:} Start slowly. Increase the frequency and duration of daily activities. Warm up. Perform low-intensity aerobic exercises. Perform strength training. Do yoga or pilates.
 \\
\toprule
\textbf{DC+Synth+MT (Ours):} Start slowly. Include daily activities. Warm up. Do low-intensity aerobic exercise. Strength train. Do yoga or pilates.
 \\
\toprule
\end{tabular}
}
\caption{An example output summary for Trans-Sum, Trans-Sum-R and DC+Synth+MT. Human annotators preferred the output from Trans-Sum-R and Trans-Sum over DC+Synth+MT.}
\label{tab:example_appendix_4}
\end{table*}

\subsection{Reproducibility}\label{reproducibility}
We use Fairseq \cite{ott2019fairseq} for all our experiments. We follow the hyperparmeter settings used by \newcite{lewis2019bart} for all summarization and translation models we train.\footnote{\href{https://github.com/pytorch/fairseq/blob/master/examples/bart/README.summarization.md}{Link to hyper-parameter settings used \newcite{lewis2019bart}}.} We note that we had to make some modifications to existing mBART code, to support monolingual summarization. We will make this code, along with our data pre-processing scripts, available upon acceptance. We train all our models on a single machine with four Nvidia Tesla V100 GPUs, 96 CPU cores, and 693 GB of RAM. We train all models until the validation loss no longer improves for two epochs, and use the checkpoint with the best validation loss for inference. The average runtime for each of our training runs was between three to six hours, depending on the dataset size (it was quickest for Turkish and slowest for Spanish).

All models that we report in Table \ref{tab:results} were trained using the exact same pre-trained mBART architecture ($\sim$ 680M parameters), with the same hyperparameters. For inference, we used a beam-size of five for all models. The ROUGE \cite{lin-2004-rouge} scores were computed using the official ROUGE script. \footnote{The parameters used to compute the ROUGE scores were ``-c 95 -r 1000 -n 2 -a".}

\subsection{Splitting English Data}\label{en_split}
To get a fair assessment of cross-lingual performance, we need to ensure, at a minimum, that any English article that is parallel to any test article in any of the four languages, gets mapped to the English test set. We note, however, that this is not sufficient, since there are multiple methods (articles) for each topic, and there may be some content overlap between them. Therefore, in addition to parallel articles, we also include all English articles that overlap in topic with any test article in any of the four non-English languages in the test set for English. While this way of splitting the data means we have fewer English articles for training, we opted for this as it ensures purity of the tests sets. Furthermore, it also ensures that models that learn topic-specific information will not be able to generalize to the test set, since there is minimal topical overlap. This method of splitting filtered out $\sim$ 72K examples to the test set, and left us with $\sim$ 69K examples for training and development sets.

\subsection{Data Statements}\label{data_statements}
All of the data was collected according with the terms and conditions listed on the website. We followed WikiHow's rate limit (four second delay between each request) while scraping the website. We follow the guidelines suggested by \newcite{bender-friedman-2018-data} and prepare a data statement, to the best of our ability, for the data we collect.
\subsubsection{Curation Rationale}
This dataset was collected in order to enable further research into cross-lingual and multilingual summarization. We first collected English articles from WikiHow. Each English article links to any corresponding articles that may be available in the other $17$ languages that are supported on WikiHow. We use this information to collect parallel articles between English and each of the other $17$ languages. We then align these articles using the illustrative images for each of the steps detailed in the article, since these images are unique to a given step.

\subsubsection{Language Variety}
The dataset includes articles in $18$ languages (i.e. English, Spanish, Portuguese, French,
German,
Russian,
Italian,
Indonesian,
Dutch,
Arabic,
Chinese,
Vietnamese,
Thai,
Japanese,
Korean,
Hindi,
Czech,
Turkish). The information about the varieties for the languages is not available.

\subsubsection{Speaker Demographic}
We do not have access to the demographics of the writers and editors of the articles.

\subsubsection{Annotator Demographic}
We do not collect any additional annotations for this dataset.

\subsubsection{Speech Situation}
The articles written on the website are a collaborative effort from people all over the world. Each article and summary is written and edited by 23 people, and further re-viewed by 16 people, on average, which ensures that the content is of a high-quality. A majority of the non-English articles are written by people who are fluent in both English and the target language, and are further reviewed by WikiHow's international translation team, before they are published. The articles are written as how-to guides over a wide variety of topics, and the intended audience is anyone that is interested in instructions to complete a certain task.

\subsubsection{Text Characteristics}
The articles cover $19$ broad categories including health, arts and entertainment, personal care and style, travel, education and communications, etc. The categories covered a broad set of genres and topics.

\subsection{Examples}\label{appendix_examples}
We present four additional example outputs for each of the three systems that were evaluated by human annotators. We show two examples where our system (DC+Synth+MT) was preferred, in Tables \ref{tab:example_appendix_1} and \ref{tab:example_appendix_2}, and two examples where the baselines were preferred over our system, in Tables \ref{tab:example_appendix_3} and \ref{tab:example_appendix_4}. We will make the model outputs available for all systems.

\section{Introduction}
Although there has been a tremendous amount of progress in abstractive summarization in recent years, most research has focused on monolingual summarization because of the lack of high quality multilingual resources \cite{Lewis2019BARTDS,control-over-copying:2020}. While there have been a few studies to address the lack of resources for cross-lingual summarization \cite{giannakopoulos-2013-multi,li-etal-2013-multi,elhadad-etal-2013-multi,nguyen-daume-iii-2019-global}, the datasets employed are very limited in size. Scarcity in the availability of data for cross-lingual abstractive summarization can largely be attributed to the difficulty of collecting high-quality, large-scale datasets via crowd-sourcing. It is a costly endeavor, since it requires humans to read, comprehend, condense, and paraphrase entire articles. Moreover, subjectivity in content selection, i.e. identifying the salient points of a given article, only adds to the difficulty of crowd sourcing this task \cite{nguyen-daume-iii-2019-global}.

To overcome the lack of a large-scale, high quality resource for cross-lingual summarization, we present a new benchmark dataset, \wiki,\footnote{We provide the full dataset, along with the partitions we used in our experiments for this work at: \href{https://github.com/esdurmus/Wikilingua}{https://github.com/esdurmus/Wikilingua}.} which consists of collaboratively written how-to guides with gold-standard summaries across $18$ languages. Each article and summary is written and edited by 23 people, and further reviewed by 16 people, on average, which ensures that the content is of a high-quality. The articles describe multiple methods with steps to complete a procedural task from a diverse set of topics, such as \textit{``How to Make a Creamy Coffee''}, \textit{``How to Exercise to Ease Back Pain''}. Each step contains a one sentence summary followed by a paragraph detailing the instruction, along with an image to illustrate the given instruction, as shown in Figure \ref{fig:method}. Since the ordering of steps may differ for the same article across languages, we align each step using the corresponding illustrative image, as shown in Figure \ref{fig:example}, given that each image is specific to a particular step and shared across languages.\footnote{Some newer, ``in progress" articles do not have images, and in some rare cases an article in one of the languages may use different images. We filter these out.}
\begin{figure*}[th]
\centering
    \includegraphics[width=16cm,height=5.6cm]{method.png}
    \caption{An example method \textit{``Watering, Feeding and Pruning Orchids''} from the guide for \textit{``How to Care for Orchids''}. This method consists of three steps where each step has an illustrative image, a one sentence summary (in blue), and a paragraph providing more details about this step (in red). We combine the paragraphs and summaries from all the steps in each method to create article-summary pairs.
}
\label{fig:method}
\end{figure*}

Our final dataset consists of 141,457 unique English articles. Each of the
other 17 languages has, on average, 42,783 articles that align with an article in English.
To the best of our knowledge, \wiki \space is the largest dataset with parallel articles and summaries for cross-lingual abstractive summarization to date. This further opens up avenues to explore new approaches for cross-lingual and multilingual summarization, which are currently understudied.

With the dataset in hand, we evaluate existing approaches for cross-lingual summarization as baselines. We then propose a method for direct cross-lingual abstractive summarization, leveraging synthetic data and machine translation as a pre-training step. We show that our method outperforms existing baselines, without relying on translation at inference time.

\section{Data Collection and Statistics}
WikiHow is an online resource of how-to guides on a diverse set of topics, written and reviewed by human authors. To ensure high quality content, experts are involved in the writing and reviewing process of these guides.\footnote{\href{https://www.wikihow.com/Experts}{https://www.wikihow.com/Experts}} Each page includes multiple methods for completing a multi-step procedural task along with a one-sentence summary of each step. Figure \ref{fig:method} shows an example method from the guide for  \textit{``How to Care for Orchids''}. For this guide, the method \textit{``Watering, Feeding and Pruning Orchids''} includes three steps. Each step consists of a unique illustrative image, a one sentence summary and a paragraph providing more details. We combine the paragraphs and summaries from all the steps of each method to create article-summary pairs.
Thus, the summarization task is framed as follows: \textit{given an article detailing instruction on how to complete a procedural task, produce a summary consisting of a list of steps, in the correct order}. This builds on prior work that collected data from WikiHow for monolingual summarization in English \cite{DBLP:journals/corr/abs-1810-09305}.
We note that, by design, the summaries do not incorporate any potential lead bias, which stands in contrast to single document news summarization, where position is an influential
signal  \cite{BRANDOW1995675}.

A majority of the non-English guides on this platform are translated from the corresponding English versions by human writers, who are fluent in both English and the target language. Once translated, they are further reviewed by WikiHow's international translation team, before they can be published.
Each of the guides also links to parallel guides in other languages, if available. We collected the guides for all $18$ languages available on WikiHow, and aligned the steps for each method in each guide using the illustrative images. Figure \ref{fig:example} shows an example step from the guide \textit{``How to Care for Orchids''} and its aligned step in five selected languages (English, Spanish, Turkish, Russian, and Vietnamese). This approach ensures that the alignments of the steps are high-quality since the images are unique to each step and shared across all the languages. We merged the step summaries and paragraphs for each WikiHow method as described above to obtain article-summary pairs for all the languages. Table \ref{tab:stats} provides statistics for the number of article-summary pairs in each language that are aligned with articles in English. We note that Turkish, which is the language with the fewest parallel article-summary pairs with English, is still an order of magnitude larger than
any Langauge in existing cross-lingual datasets.

\begin{figure*}[th]
\centering
    \includegraphics[width=16cm,height=5.6cm]{orchid.png}
    \caption{An example step from the guide for \textit{``How to Care for Orchids''}, across five selected languages (top to bottom: English, Spanish, Turkish, Russian and Vietnamese). This shows the summary for the step (bold text), along with the first sentence of the paragraph. Note that the images are the same across the different languages. To get final article-summary pairs, we combine the paragraphs and summaries from all steps in a method.
}
\label{fig:example}
\end{figure*}

\begin{table*}[ht]
\centering
\setlength{\tabcolsep}{5pt}
{
\begin{tabular}{lrrrr}
\toprule
& Num. Languages & Num. Summaries & Summary length & Article length\\
&              &   (average)           & (average)      & (average)\\
\toprule
MultiLing'13  & 40 & 30 & 185  & 4,111\\
MultiLing'15  & 38 & 30 & 233 & 4,946 \\
Global Voices  & 15 & 229 &  51 & 359   \\
\wiki & 18 &  42,783  &  39 & 391 \\

\toprule
\end{tabular}
}
\caption{Comparison of \wiki \space with the existing multilingual summarization datasets. \textit{Num. languages} indicates number of languages covered in each dataset. \textit{Num. summaries} indicates average number of articles per language. \textit{Summary length} and \textit{Article length} corresponds to average number of tokens in summaries and articles respectively.}
\label{tab:comparison_data}
\end{table*}

\begin{table}[ht]
\centering
\setlength{\tabcolsep}{4pt}
{
\begin{tabular}{lcr}
\toprule
Language  & ISO 639-1 & Num. parallel  \\
\toprule
English & en & 141,457 \\
Spanish & es & 113,215\\
Portuguese & pt & 81,695 \\
French & fr & 63,692\\
German & de & 58,375  \\
Russian & ru & 52,928 \\
Italian & it & 50,968  \\
Indonesian & id & 47,511 \\
Dutch & nl & 31,270\\
Arabic & ar & 29,229\\
Chinese & zh & 18,887 \\
Vietnamese & vi & 19,600 \\
Thai & th & 14,770  \\
Japanese & ja & 12,669  \\
Korean & ko & 12,189 \\
Hindi & hi & 9,929 \\
Czech & cs & 7,200 \\
Turkish & tr & 4,503\\
\toprule
\end{tabular}
}
\caption{Statistics for \wiki. Num. parallel corresponds to the number of articles with a parallel article in English. There are in total 141,457 English article-summary pairs in our dataset.}
\label{tab:stats}
\end{table}

\section{Existing Multilingual Abstractive Summarization Datasets}

There have been a few datasets created for multilingual abstractive summarization tasks in recent years, which we describe in this section.

\textbf{MultiLing'13 and '15.} Multiple versions of the MultiLing dataset have been collected by the organizers of MultiLing Workshops \cite{giannakopoulos-2013-multi,elhadad-etal-2013-multi,kubina-etal-2013-acl}. The MultiLing'13 dataset includes summaries of 30 Wikipedia articles per language, describing a given topic. For MultiLing'15, an additional $30$ documents were collected for evaluation purposes \cite{giannakopoulos-etal-2015-multiling}. We note that while this dataset contains article and summaries in several
languages
there are no parallel articles or summaries, which makes it difficult to use this dataset for cross-lingual evaluation.

\textbf{Global Voices.} \newcite{nguyen-daume-iii-2019-global} collected social network descriptions of news articles provided by
Global Voices.\footnote{\href{https://globalvoices.org/}{https://globalvoices.org/}} These descriptions, however, are not written with the purpose of summarizing the article content but rather to draw user clicks on social media;
therefore, they have a lower coverage of the original article than a good summary would.
To address this problem, the authors crowd-source a small set of summaries, in English, for 15 languages.
We report
statistics
only on the crowd-sourced summaries, given the click-bait nature of the social media descriptions. Note that unlike our dataset, this one contains summaries only in English, which makes it difficult to evaluate cross-lingual summarization into other languages.

Statistics for the datasets are provided in Table \ref{tab:comparison_data}. \wiki \space is similar to Global Voices in terms of article and summary length while MultiLing articles and summaries are longer. All three existing datasets are limited in size in comparison to \wiki.  Furthermore, our dataset includes articles on a wide-range of topics and the average number of articles per language is two orders of magnitude larger than Global Voices, which is the largest dataset to date for cross-lingual evaluation. The Data Statement \cite{bender-friedman-2018-data} for our dataset can be found in Appendix \ref{data_statements}.

\begin{table}[ht]
\centering
\setlength{\tabcolsep}{5pt}
\scalebox{1}{
\begin{tabular}{lrrr}
\toprule
 &  Train & Validation &  Test  \\
\toprule
Spanish & 81,514 & 9,057 & 22,643\\
Russian  & 38,107 & 4,234 & 10,586 \\
Vietnamese  & 9,473 & 1,052 &  2,632\\
Turkish  &  3,241 & 360 & 901\\
\toprule
\end{tabular}
}
\caption{Number of examples in Train/Validation/Test splits per language.}
\label{tab:data_size}
\end{table}

\section{Cross-lingual Experiments}
Following the prior work in cross-lingual abstractive summarization \cite{nguyen-daume-iii-2019-global,ouyang-etal-2019-robust}, we aim to generate English summaries from non-English articles, as an initial study. We experiment with five languages (i.e.\ English, Spanish, Russian, Turkish, and Vietnamese) covering three language families  (i.e.\  Indo-European, Ural-Altaic and Austroasiatic). We split the data for each of the four non-English languages into train/dev/test splits. When splitting the English data, we ensure that all articles from the same topic as  test articles in any of the four non-English languages, are included in the test set. This leaves us with $\sim$ 69K English articles that we randomly split into train and dev set (90/10 split). See Appendix \ref{en_split} for more information.

We use large, pre-trained language models as a starting point for our experiments, given their success on a variety of downstream Natural Language Processing tasks \cite{devlin-etal-2019-bert}, including state of the art results for text summarization \cite{lewis2019bart, liu-lapata-2019-text}. In particular, we use mBART \cite{liu2020multilingual}, which is a multi-lingual language model that has been trained on large, monolingual corpora in 25 languages. The model uses a shared sub-word vocabulary, encoder, and decoder across all 25 languages, and is trained as a denoising auto-encoder during the pre-training step. \newcite{liu2020multilingual} showed that this pre-training method provides a good initialization for downstream machine translation tasks, particularly in lower resources settings, making this an ideal starting point for our cross-lingual summarization experiments. We also ran initial experiments with non-pretrained transformer models, but the results were significantly worse than those with the pre-trained models.

We fine-tune mBART for both monolingual and cross-lingual summarization as a standard sequence-to-sequence model, where the input document is represented as a sequence of tokens (sub-word units), with a special separator token between each sentence, and a language indicator token at the end of the document. The output summary is represented in a similar manner, with a language indicator token at the beginning of the sequence, to prime
the decoder for generation in the target language, as shown in Figure \ref{fig:model}. We use Fairseq \cite{ott2019fairseq} for all our experiments, and we follow the hyper-parameter settings that were used by \newcite{lewis2019bart} to fine-tune BART for monolingual summarization in English. See Appendix \ref{reproducibility} for more details.

\begin{figure*}[th]
\centering
    \includegraphics[width=16cm,height=2.6cm]{model.png}
    \caption{An example showing the fine-tuning procedure for cross-lingual summarization from Spanish to English.}
\label{fig:model}
\end{figure*}

\subsection{Baselines}
We evaluate the following baseline approaches for cross-lingual summarization on our data:

\textbf{lead$_{n}$:} copies first $n$ sentences from the corresponding parallel English source articles. We report results for $n=3$ since it performs the best.

\textbf{Summarize-then-translate (Sum-Trans):} We fine-tune mBART for monolingual summarization in the source language, and then at inference time, we summarize the article and then translate the summary into the target language. This approach is useful when the source language is higher resource for the summarization task, since it requires translating summaries, which tend
to
be much shorter than the actual articles, which means fewer opportunities for translation errors.

\textbf{Translate-then-Summarize (Trans-Sum):} We fine-tune mBART for monolingual summarization in the target language and at inference time, we translate the source language articles into the target language, and then summarize the translation. This approach is useful when the target language is higher resource for the summarization task, though translating entire articles provides more opportunities for translation errors.

\textbf{Trans-Sum-R:} This method, a variation of the translate then summarize method above, first performs a round-trip translation of articles from, and back to, the target language, through the source language, to get noisy articles in the target language. The noisy articles are then paired with the original, clean summary, to train a summarization system in the target language \cite{ouyang-etal-2019-robust}. The summarization system, in this case, can account for potential noise in the translated source article, by learning to generate clean summaries from noisy articles.
For all baselines that require translation, we used the Amazon Web Services (AWS) Translate service, which is among the state of the art Neural Machine Translation systems.\footnote{\href{https://aws.amazon.com/translate/}{https://aws.amazon.com/translate/}}

\textbf{Trans-Sum-G:} This model is the same as the \textbf{Trans-Sum} model except that at inference time, we use the gold translation of the source language article instead of the machine translated one. This is an oracle system that represents the performance we could expect if we had no translation errors. Thus the drop in performance from \textbf{Trans-Sum-G} to \textbf{Trans-Sum} or \textbf{Trans-Sum-R} can be attributed to translation errors.

\begin{table*}[ht]
\centering
\setlength{\tabcolsep}{1.5pt}
\scalebox{1}{
\begin{tabular}{lc||c||c||c}
\toprule
& Es-En & Tr-En & Ru-En & Vi-En \\
\toprule
Trans-Sum-G
& $41.66 / 18.64 / 35.07$

& $45.82 / 22.42 / 39.05$
& $40.98 / 18.27 / 34.74$
& $41.37 / 18.56 / 35.22$\\
\toprule
lead$_{3}$
& $24.35 / 06.03 / 16.39$
& $24.55 / 05.98 / 16.49$
& $23.43 / 05.56 / 15.81$
& $22.92 / 05.41 / 15.47$
\\
Sum-Trans
& $36.03 / 13.02 / 29.86$
& $31.57 / 10.45 / 24.76$
& $29.75 / 08.83 / 24.36$
& $26.95 / 07.04 / 21.62$
\\
Trans-Sum
& $37.16 / 14.25 / 31.04$
& $41.06 / 17.72  / 34.53$
& $33.59 / 11.60 / 28.15$
& $34.77 / 12.37 / 29.27$
\\
Trans-Sum-R
& $38.13 / 14.95 / 31.96$
& $42.33 / 18.79 / 35.81$
& $34.64 / 12.58 / 29.18$
& $36.29 / 13.21 / 30.57$\\
\toprule
DC
& $38.30 / 15.37 / 32.40^\dag$
& $33.68 / 12.74 / 27.62$
& $32.91 / 11.83 / 27.69$
& $31.89 / 11.07 / 26.36$
\\
DC+Synth
& $40.00 / 16.38 / 33.48^\dag$
& $41.76 / 18.84 / 35.78$
& $36.82 / 14.41 / 31.18^\dag$
& $36.48 / 14.29 / 30.96^\ddag$
\\
DC+Synth+MT
& $\textbf{40.60} / \textbf{16.89} / \textbf{34.06}^\dag$
& $\textbf{42.76} / \textbf{20.47} / \textbf{37.09}^\ddag$
& $\textbf{37.09} / \textbf{14.81} / \textbf{31.67}^\dag$
& $\textbf{37.86} / \textbf{15.26} / \textbf{32.33}^\dag$
\\

\toprule
\end{tabular}
}
\caption{Cross-lingual summarization results. The numbers correspond to $\text{ROUGE-1}/\text{ROUGE-2}/\text{ROUGE-L}$ F1 scores respectively. \dag \space indicates where ROUGE-L F1 is significantly better than all baselines, and \ddag \space indicates where ROUGE-L F1 is significantly better than all baselines except Trans-Sum-R. We use Welch's t-test, and use $p < 0.01$ to assess significance.}
\label{tab:results}
\end{table*}

\subsection{Direct Cross-lingual Summarization}
Most work in cross-lingual summarization has relied on different variations of a two-step approach to cross-lingual summarization, i.e. translation and summarization. Besides the issue of error propagation, another major drawback of such approaches is that they rely on translation at inference time, which makes inference costly as it requires running both a translation system and a summarization system, in sequence. In a real-world scenario, such systems would have a recurring latency and monetary cost for each inference request. Therefore, it is preferable to have cross-lingual summarization methods that do not rely on running an additional translation system at inference time.

The popularity of existing two-step approaches for cross-lingual summarization can largely be attributed to the data that is available -- there are plenty of resources for both machine translation and monolingual English summarization as separate tasks. However, resources that contain parallel articles in multiple languages, with corresponding parallel summaries are scarce. Since our dataset has gold standard translations between English and the other languages, it allows us to explore methods for direct cross-lingual summarization, and measure how they stack up against existing baselines. Furthermore, since we have gold translations, we can directly measure the drop in performance due to translation errors for translate-then-summarize, for each language pair, and see how much of that can be recovered by proposed methods.

For direct cross-lingual summarization, we fine-tune mBART with input articles from the source language, and summaries from the target language \textbf{(DC)}. This setting requires that the model learn both translation and summarization, which requires a large amount of cross-lingual training data. To overcome this, we first propose to generate additional synthetic data by translating the English training articles into the target language \textbf{(DC+Synth)},
using AWS Translate, and pairing them with the original summary in English. Translating training data has been shown to be an effective strategy for cross-lingual transfer for text classification and sequence labeling tasks \cite{Schuster2019CrosslingualTL}.
We note that while this method still relies on machine translation, the cost of translation is shifted to training time, and thus is a
one-time cost.

Since a cross-lingual summarization model needs to learn how to translate salient information from one language to another, we hypothesize that training the model for machine translation can improve performance of cross-lingual summarization. Therefore, we propose a two-step fine-tuning approach, where we first fine-tune the mBART model for document level machine translation from the source language into English, and then we further fine-tune the model for cross-lingual summarization \textbf{(DC+Synth+MT)}. Similar to above, since we only have a limited amount of parallel document pairs in our dataset, we translate English documents into the source language to create additional parallel data. This method of back-translation to create additional parallel data has been shown to be effective in improving the performance of neural machine translation systems \cite{sennrich-etal-2016-improving, hoang-etal-2018-iterative, edunov-etal-2018-understanding}.\footnote{While back-translation typically uses an intermediate training checkpoint to create synthetic data, we instead use AWS translate.}
\begin{table*}[t]
\centering
\setlength{\tabcolsep}{3pt}
\scalebox{0.9}{
\begin{tabular}{p{15cm}}
\toprule
\textbf{Topic:} How to critique a speech: Assessing the Delivery. \\
\toprule
\textbf{Article:} Does the speaker talk in a way that makes you want to keep listening, or is it easy to tune out? ...  The way the speaker holds him or herself should project confidence and charisma, making the audience feel engaged and included ... Too many "ums", "likes" and "uhs" take away from a speaker's credibility ... A great speaker should have memorized the speech long in advance ... Look for signs that the speaker is nervous so you can offer a critique that will help him or her improve next time ...  \\
\toprule
\textbf{Reference:} Listen to the speaker's voice inflections. Watch the speaker's body language. Listen for filler words. See if the speech was memorized. Assess how the speaker manages anxiety.  \\
\toprule
\textbf{Trans-Sum:} Keep the listener's attention. Maintain good posture. Memorize speech beforehand. Identify signs of nervousness.\\
\toprule
\textbf{Trans-Sum-R:} Recognize the listener's needs. Pay attention to the posture of the speaker. Memorize the speech. Recognize the signs of nervousness. \\
\toprule
\textbf{DC+Synth+MT (Ours):} Pay attention to the way the speaker is speaking. Notice the way the speaker uses body language. Keep track of the words they say. Remember what they have to say. Watch for signs of nervousness. \\
\toprule
\end{tabular}
}
\caption{An example output summary for Trans-Sum, Trans-Sum-R and DC+Synth+MT. Human annotators preferred the output from DC+Synth+MT.}
\label{tab:example_analysis}
\end{table*}

\section{Results and Analysis}

Table \ref{tab:results} shows ROUGE scores \cite{lin-2004-rouge} for the baselines and proposed cross-lingual approaches. We observe that the lead baseline performs poorly for this task, unlike in the news domain where it's shown to be a strong baseline
\cite{BRANDOW1995675}.

When comparing the performance of Trans-Sum vs.\ Sum-Trans, we find that performance depends on the amount of summarization data available in the source language. Similar to previous work \cite{ouyang-etal-2019-robust}, we find that Tran-Sum works
significantly better when the amount of data in the source language is limited. However, as source language training data size increases, we see that the gap in performance decreases, as in the case of Spanish, which is similar in size to English, vs.\ Turkish, which is the lowest resource language for summarization in our dataset. This suggests that when the source language data is comparable in size or larger than the target language data, Sum-Trans approach may be worthwhile to consider, as suggested by \newcite{wan-etal-2010-cross}, since it is more cost effective (translating summaries instead of whole articles) and may avoid error propagation from translation systems.

Amongst the baseline methods, Trans-Sum-R works the best. It consistently does better than Trans-Sum baseline, suggesting that round-trip translation to create noisy data can be an effective way to make the model more robust to translation errors at inference time. Since we have gold translations \textbf{(Trans-Sum G)} for each of the articles, we can measure the translation error in the Trans-Sum system. We see that on average, the round-trip translation method is able to recover about 22\% of the performance loss due to translation errors.

For direct cross-lingual summarization, we find that the performance of the base model (DC) is worse than the translate-then-summarize baselines for all languages except Spanish, where it is better. This suggests that direct cross-lingual summarization is a difficult task and requires a larger amount of data, even with a pre-trained mBART model as a starting point. Once we add some synthetic data (DC+Synth), we see the performance improves significantly, especially for the lower resource languages (Tr and Vi), which are on par with the best baseline model. Note that the DC+Synth models would still be preferable, even over the best baseline, as they give similar performance while being much more cost effective for inference.

Finally, we see that fine-tuning the mBART model for document-level machine translation, before fine-tuning it for cross-lingual summarization, further improves the performance for all languages. This variant (DC+Synth+MT) performs significantly better than all baseline systems for Spanish, Russian and Vietnamese. For Turkish, the performance of DC+Synth+MT is statistically the same as Trans-Sum-R;
we note, however, that our model is significantly better than the Trans-Sum baseline, while the Trans-Sum-R model is not.

\subsection{Human Evaluation}
We ask human annotators on Mechanical Turk to evaluate the generated summaries for fluency and content overlap with the gold reference summary.\footnote{The reference was only shown when evaluating for content overlap, and not for fluency evaluation.} We randomly sample $100$ articles per language and generate summaries using Trans-Sum, Trans-Sum-R and DC+Synth+MT.  Each annotator is shown all three summaries for the same article, along with the reference, and asked to score the summaries for fluency and content on a scale from 1 to 3.
Each of the examples was evaluated by three annotators. To ensure for quality, we filter out annotators with a low agreement score with other annotators who performed the same tasks. The average pairwise agreement between annotators is 56.5\%.

Table \ref{tab:human_eval} shows that human annotators find all three systems relatively fluent overall. This can be attributed to the use of mBART, which has been pre-trained on large amounts of monolingual data. While there is no significant difference between Trans-Sum-R and DC+Synth+MT, we note that DC+Synth+MT scored significantly higher than Trans-Sum, while Trans-Sum-R is statistically the same as Trans-sum. In terms of content overlap with the reference, we find that DC+Synth+MT model scored significantly better than both the baseline systems (p$\leq$0.05), which validates the ROUGE score improvements we show in Table \ref{tab:results}. Note that the baselines systems are statistically the same in terms of content. Table \ref{tab:example_analysis} shows an example of an article and corresponding output summaries for each of the three systems evaluated. We can see that all the system generated summaries are fluent, however DC+Synth+MT has better overlap with the content in the reference summary.\footnote{More examples are provided in Appendix \ref{appendix_examples}.}

\begin{table}[ht]
\centering
\setlength{\tabcolsep}{3pt}
\scalebox{0.9}{
\begin{tabular}{l r r}
\toprule
Model & Fluency & Content \\
\toprule
Trans-Sum & 2.61 & 2.07 \\
Trans-Sum-R & 2.62 & 2.09 \\
DC+Synth+MT &  \textbf{2.67} & \textbf{2.19} \\
\toprule
\end{tabular}
}
\caption{Human evaluation scores on a scale of 1-3.}
\label{tab:human_eval}
\end{table}

\section{Related Work}

\textbf{Abstractive Summarization.} The majority of research in abstractive summarization has focused on monolingual summarization in English \cite{gehrmann2018bottom, control-over-copying:2020, xsum-emnlp}. \newcite{rush-etal-2015-neural} proposes the first neural abstractive summarization model using an attention-based convolutional neural network encoder and a feed-forward decoder. \newcite{chopra-etal-2016-abstractive} shows improvements over this model using a recurrent neural network for the decoder. \newcite{nallapati-etal-2016-abstractive} shows further improvements by incorporating embeddings for linguistic features such as part-of-speech tags and named-entity tags into their model, as well as a pointer network \cite{NIPS2015_5866} to enable copying words from the source article. \newcite{see-etal-2017-get} extends this model by further incorporating a coverage penalty to address the problem of repetitions in the generated summary.

\newcite{chen-bansal-2018-fast} takes a two stage approach to abstractive summarization by learning an extractor to select salient sentences from the articles, and an abstractor to rewrite the sentences selected by the extractor. They further train the extractor and abstractor end-to-end with a policy-gradient method, using ROUGE-L F1 as the reward function. Recently, pre-trained language models have achieved the state of the art results in abstractive summarization  \cite{lewis2019bart,liu-lapata-2019-text, control-over-copying:2020}. Therefore, we use mBART \cite{liu2020multilingual} for all the baselines and our direct cross-lingual models.

\textbf{Cross-lingual Abstractive Summarization.} \newcite{wan-etal-2010-cross} proposes summarize-then-translate and translate-then-summarize as approaches for doing cross-lingual summarization. They suggest that summarize-then-translate is preferable because it is computationally less expensive since it translates the summary rather than article, and therefore is less prone to error propagation from translation systems.
As we show in our work, however, this approach requires a large amount of training data in the source language to build an effective summarization system. On the other hand, translate-then-summarize approach relies on having an accurate translation system and a large amount of summarization training data in the target language. Although translate-then-summarize \cite{10.1145/979872.979877}  and summarize-then-translate \cite{Lim2004MultiDocumentSU,orasan-chiorean-2008-evaluation,wan-etal-2010-cross} are widely used approaches in prior studies, they are prone to error propagation.
\newcite{ouyang-etal-2019-robust} propose a variant of the translate-then-summarize approach to cross-lingual summarization, by doing a round-trip translation of English articles through the source language to get noisy English articles. They then train on noisy article and clean summary pairs, which allows them to account for potential translation noise.

There is limited prior work in direct cross-lingual summarization. \newcite{10.1109/TASLP.2018.2842432} propose zero-shot cross-lingual headline generation to generate Chinese headlines for English articles, via a teacher-student framework, using two teacher models.
\newcite{duan-etal-2019-zero} propose a similar approach for cross-lingual abstractive sentence summarization. We note that our approach is much simpler and also focuses on a different kind of summarization task.

\newcite{zhu-etal-2019-ncls} use round-trip translation of large scale monolingual datasets \cite{10.5555/2969239.2969428,zhu-etal-2018-msmo, hu-etal-2015-lcsts} to generate synthetic training data for their models, and train a multi-task model to to learn both translation and cross-lingual summarization. We tried their approach on our data, using the code provided,\footnote{\href{https://github.com/ZNLP/NCLS-Corpora}{https://github.com/ZNLP/NCLS-Corpora}}
but the results were worse than all baselines except lead.\footnote{This model gets
ROUGE-L F1 scores of $22.49$, $23.38$, $20.79$, $19.45$ for Spanish, Turkish, Russian and Vietnamese respectively.} We suspect that this may be due to the amount of training data, as their synthetic dataset was much larger than ours (1.69M pairs for Zh-En). An extension of their approach would be to incorporate multi-task training for pre-trained mBART, which we leave for future work.
Scarcity of cross-lingual summarization data has limited prior work to a few languages, and mostly in the news domain \cite{wan-etal-2010-cross, wan-2011-bilingual, yao-etal-2015-phrase, 7502066, wan2019cross}. While there is some existing work trying to address this
\cite{nguyen-daume-iii-2019-global}, the proposed dataset is still limited in size, and contains summaries only in English. We address this limitation by proposing a new benchmark dataset.

\section{Conclusion}
We present a benchmark dataset for cross-lingual and multilingual abstractive summarization. We then evaluate existing methods in cross-lingual abstractive summarization. We further propose an end-to-end method for direct cross-lingual summarization and show that it achieves significantly better performance than the baselines while being more cost effective for inference.

Our new benchmark dataset opens up interesting new directions for research in summarization. We would like to further explore multi-source cross-lingual summarization architectures, i.e. models that can summarize from multiple source languages in to a target language. Another interesting avenue would be to explore the feasibility of multilingual summarization, i.e. building models that summarize articles from any language to any other language for a given set of languages.

\section{Acknowledgements}
We would like to thank Chris Kedzie and the anonymous reviewers for their feedback. This research is based on work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract FA8650-17-C-9117. This work is also supported in part by National Science Foundation (NSF) grant 1815455 and Defense Advanced Research Projects Agency (DARPA) LwLL  FA8750-19-2-0039. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, NSF, DARPA or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.

\bibliographystyle{acl_natbib}

redpajama_set_name: RedPajamaArXiv


You're here for the desert culture, which includes walks with Kalahari bushmen and sleeping inside a tent that's a designated Natural History Museum.
Set on the arid Makgadikgadi Pans, in the Kalahari Desert, Jack's Camp is where those who are hungry for the silence of the desert go, who lust for its adventure and poetry, and for the oriental-rug-lined tents of this authentic African camp.
It has a sort of sit-cross-legged-on-the-ground feel, with its mounds of pillows, rugs, and low tables in the common spaces. But as relaxed as the vibe may be, Jack's Camp is one of the most reputable in the industry, as much for its desert game viewing as for the romance of its design. It has been designated a Natural History Museum of Botswana due to its collection of artifacts and can, at times, feel like you are in an exact replica of storied safari tents from a century ago.
It's a beaut. Each of the ten tents are done in a classic style, with early settler touches. This means Persian rugs, ostrich feathers, and brass lamps, as well as the obligatory stacks of old history and exploration books. Raised platform tents and terraces mean solid views over the desert.
It's a nice counter to the lush Okavango; the desert ecosystem means you spot animals that have evolved into subspecies over millions of years here, like black maned lions. There's a ton of ostrich and springbok, too. Night drives mean you can try your luck at spotting elusive creatures like aardvark, while opportunities to walk the bush with a Kalahari Bushman (an increasing rarity) are also possible. During dry season (May through October) you can safely 4WD over the desert without harming the terrain.
You eat in a (largely artifact-filled, beautifully adorned) mess hall, but the menu and quality are on-point. Think fresh tomato and basil soups, ostrich piccata, and homemade chicken pies. It's communal dining, which means meals are a time for story telling and exchanges with your guides and other guests.


Elinet's Intermediate Certificate Program covers 100 lessons, each containing five pages of extremely valuable learning materials, including recorded audio dialogues (MP3 files) touching on hundreds of topics and issues.
over 5,100 of the most popular and widely used English nouns, adjectives, verbs and verb combinations, adverbs, conjunctions, prepositions, etc.
Students are required to complete a number of exercises and special assignments requiring both independent processing of lesson content by the students and interaction with their tutor.
The Intermediate Certificate Program is designed for those who have completed the Basic Certificate Program or anyone who wishes to become fluent in English and expand their existing basic speaking, reading and comprehension skills in the shortest time possible.
Listening to recorded audio dialogues (MP3 files) featuring native English speakers helps students develop their understanding of spoken, fluent English language used in a wide range of situations. At the same time students become accustomed to two major varieties of English pronunciation – North American (used in Canada and the US) and British (used in the United Kingdom and other British Commonwealth countries).
Dialogue: INVITING FRIENDS TO A PARTY. Useful Phrases and Models: Satisfaction with Present and Past Facts, Events. Inviting Guests to a Party, Lunch, Dinner. Accepting an Invitation. Emphatic Confirmation. time expressions. fairly structures. Verbal throw combinations. Polite Enquiry, Asking for Information. Sharing Information, Explaining Actions, Decisions. Saying Good-Bye. List of new words. 3 exercises.
Dialogue: A ROMANTIC NIGHT OUT. Useful Phrases and Models: Thanking for a Gift or Flowers. Enquiring about Someone's Plans. Inviting Someone Out to Lunch, Dinner. Enthusiastic Approval, Gladly Agreeing to an Offer, Invitation, Idea; Gladly Accepting an Invitation. Approving Ideas, Offers, Suggestions; Positive Reaction. Making a Decision. Approximate Time. List of new words. 2 exercises.
Dialogue: PLANNING A NICE DINNER AFTER A BUSY WEEK. Useful Phrases and Models: Invitations, Suggestions, Ideas. Politely Declining an Offer or an Invitation. Reaction to a Refusal. Repeated Invitations, Insisting. Agreeing to an Idea, Accepting an Invitation after Some Thinking or Hesitation. List of new words. 2 exercises.
Dialogue: SHOPPING FOR MEN'S WEAR. Grammar: Adjective Order Before Nouns: Common Rules. Useful Phrases and Models: Satisfaction with an Item, Confirming Intention to Buy; Making a Final Decision. Asking for a Salesperson's Opinion. Talking about Style, Colour, Needs. Useful Words: Clothing - Men's Wear, Ladies' Wear, Children's Wear, Athletic Wear, etc. Footwear. Details of Clothing. Colours, Shades and Tints. List of new words. 5 exercises.
Dialogue: SHOPPING IN A REFURBISHED SUPERMARKET: Selection and process. Grammar: some / any in Questions. Correct usage of else. Useful Phrases and Models: Questions at Bakery, Meat / Deli Counter. Positive Impressions in the Present and in the Past. Conceding. Criticism. price vs. prize. Useful Information: When You're in a Hurry. List of new words. 3 exercises.
Dialogue: DAMAGE DONE TO A RENTAL PROPERTY. Grammar: Passive Verb Forms in the Simple Present Tense. Useful Phrases and Models: Describing General and Specific Problems with Home Appliances. Enquiring About Problems. Useful Information: Important Tips on Renting in North America. List of new words. 3 exercises.
Dialogue: LOOKING AT A HOUSE FOR SALE BY THE OWNER. Grammar: Passive Verb Forms in the Simple Past Tense. Useful Phrases and Models: Satisfaction. Good Timing for an Action. Personal Criticism. Most popular well- + past participle combinations. adverb + past participle: Popular Compound Structures. Useful Words: More Real Estate Terms. List of new words. 4 exercises.
Dialogue: RENOVATING AND REMODELLING AN OLD HOUSE. Grammar: Suffix -less. Correct usage of prepositions with made. Useful Phrases and Models: Examples Illustrating Arguments. Direct Suggestion, Advice. Suggestions in the Past. Summing Up. Correct usage of dream of and dream about. List of new words. 5 exercises.
Dialogue: GIVING INSTRUCTIONS. Useful Phrases and Models: Necessity; Requirements. Giving Commands, Instructions; Explaining Do's and Dont's. Obligations, Duties. Advisability, Suggesting a Course of Action. Asking for Instructions, Requesting More Information. Lack of Necessity. Prefix over-. List of new words. 3 exercises.
Dialogue: RENOVATING A FAMILY HOME. Grammar: Passive Verb Forms in the Present Perfect Tense. Using get + object + past participle for Results of Actions. Useful Phrases and Models: Most Common feel Combinations. List of new words. 4 exercises.
Dialogue: A CHANCE ENCOUNTER WITH AN OLD FRIEND. Useful Phrases and Models: Approval, Positive Opinion. Favourite Activities, Interests. Conversational Comments: Showing Interest, Surprise. Getting a Message Across. Meeting a Friend After a Long Time: the first reaction, questions, time passed. Difference between economic and economical. Popular run combinations. List of new words. 3 exercises.
Dialogue: MEETING AN OLD FRIEND IN A SIDEWALK CAFE. Grammar: Duration of Time. Useful Phrases and Models: Meeting a Friend After a Long Time: Greetings, Comments, Questions and Typical Reactions. Time expressions. Marriage expressions. Reaction to Good News: Marriage or Baby. List of new words. 2 exercises.
Dialogue: TWO FRIENDS TALKING ABOUT COMMON FRIENDS. Useful Phrases and Models: Meeting an Old Friend: Questions about Current Activities, Typical Reactions. Reaction To News: Surprise, Amazement. Contacts with Mutual Friends. Sharing Latest Available Information. Confirming Information. Wishing Success, Luck. Saying Good-Bye. Various time expressions. List of new words. 2 exercises.
Dialogue: GOING THROUGH DIFFICULT TIMES IN LIFE; PROBLEMS AT WORK: Supporting a friend in a time of need. Useful Phrases and Models: Finding Similarities. Sharing a Similar Experience. Sharing Bad News. Describing Shock, Emotional Pain, Trauma. Offering Comfort, Support. Understanding Personal Situation. Inability to Change Things. Improving Health, Personal or Business Situation. Reaction; Good Wishes. List of new words. 2 exercises.
Dialogue: SOCIAL LANGUAGE: COMING TO A BIRTHDAY PARTY. Grammar: Manner of Action. Useful Phrases and Models: Degree and Purpose. Introducing Two Strangers; Possible Situations During Introduction; Typical Greetings. Visiting Someone's House: Comments of Approval, Compliments. Welcoming a Guest in Your Home. Polite Reaction to a Gift. Addressing Your Guests. Polite Invitations, Offers. Satisfaction with Food; Complimenting Your Host (Hostess) on His (Her) Cooking. List of new words. 3 exercises.
Dialogue: SOCIAL LANGUAGE: EATING IN A CAFE: Talking about food, mutual friends, city life. Grammar: Most Popular Intensifiers and Adverbs of Degree for Gradable Adjectives, Adverbs, and Verbs. Adverbs of Time. Useful Phrases and Models: Common Friends, Acquaintances. Degree of Familiarity. Surprise, Disbelief; Asking to Confirm Information. List of new words. 3 exercises.
Dialogue: SOCIAL LANGUAGE AT A PARTY: Talking about food, real estate, travel. Grammar: Using must with Progressive Verb Forms. Useful Phrases and Models: Thanking Your Host / Hostess After a Party. Apologizing When Leaving a Person. Ending a Conversation Politely. Inviting Friends to Your Home. Arranging a Specific Time for a Visit. List of new words. 2 exercises.
Dialogue: WINNING THE LOTTERY. Grammar: must have + Past Participle. Useful Phrases and Models: Approaching Strangers, Attracting Someone's Attention; Asking Permission to Join a Stranger or a Company. Passing Along Information. Saying Good-Bye; Leave Taking: Various Contexts. Social Language: Reaction to Information; Comments of Interest, Support, Understanding. Surprise, Amazement. List of new words. 2 exercises.
Dialogue: IN A SMALL ETHNIC RESTAURANT. Grammar: Inverted Word Order. Useful Phrases and Models: Amazement, Surprise, Disbelief. Asking to Confirm Information. Agreement. Support. Sharing the Same Tastes or Views. Saying Good-Bye; Leave Taking: Various Context. Expressing Emphasis in Informal Speech. List of new words. 4 exercises.
Dialogue: HAVING SOME COFFEE AND CAKE ON A COLD DAY. Useful Phrases and Models: Unusual Facts, Aspects; Things Worthy of Note. Explaining Typical, Real-Life Situations. Social Language: Host (Hostess) Offering Different Food Choices; Guest Choosing. Offering More Food; Refusing Offers of Food / Drinks. Typical Reactions to Refusal. Asking Permission to Have Some More Food. Refusing to Eat Certain Types of Food. Intention to Leave; Typical Reaction. List of new words. 2 exercises.
Dialogue: A YOUNG COUPLE DISCUSSING AN EXCITING JOB OFFER AND FUTURE PLANS. Grammar: The Present Perfect Progressive Tense. Useful Phrases and Models: Generalizing, Summing It Up. List of new words. 3 exercises.
Dialogue: GOING BACK TO WORK AFTER THANKSGIVING DAY. Office situations. Useful Phrases and Models: Seeking Agreement, Support from a Listener. Checking Understanding. Confirming Understanding, Showing Support. Agreeing with the Speaker. Natural Conclusion: Reacting to Information. Unsolicited Opinion, Criticism. Making Conversation: Comments Displaying Support, Interest. Encouraging the Speaker. Difference between feel about and feel + infinitive. List of new words. 2 exercises.
Dialogue: A NEW NEIGHBOUR. Useful Phrases and Models: Explaining. Telling a Story: Wandering Away from the Subject for a While; Adding Important Information Along the Way. Interrupting the Speaker; Asking for Patience. Politely Agreeing with the Speaker; Confirming Information. Impressions, Predictions, Probable Events. Commenting on the Story. Expressing Amazement. Apologizing When You Need to Leave; Ending Conversation Politely. List of new words. 3 exercises.
Dialogue: DISAPPOINTING NEWS. Grammar: Using the past tense for Unreal Comparisons, Imaginary Situations. Useful Phrases and Models: Delivering Disappointing, Sad News. Breaking Bad News Gently. Expressing Disappointment. Reaction to a Bad Experience, Unpleasant News. Suggestions to Act or Change Activities; Choosing between Two Options. List of new words. 4 exercises.
Dialogue: RELOCATING TO ANOTHER CITY; COST OF LIVING. Useful Phrases and Models: Positive Aspect vs. Negative Aspects; Pluses and Minuses. Explaining Complexity of a Situation, Issue. Introducing Unpleasant Aspects. Cause for Concern; Existing Problems. Asking for a Chance to Talk; Typical Reaction. List of new words. 3 exercises.
Dialogue: WHEN YOU LOVE WHAT YOU DO FOR A LIVING. Useful Phrases and Models: Job, Career, Occupation: Typical Questions and Reply Structures. Job, Occupation: Common Structures. Rare Opportunity. Comparison, Similarity. Expressing a Careful Opinion, Estimate; Speculating, Guessing. Future Events. List of new words. 4 exercises.
Dialogue: CANADIAN BUSINESS SUCCESS: The owner of a company shares his advice on how to make it big in the world of business. Grammar: Amount, Quantity. Intensity. Difficulty. Useful Phrases and Models: Asking Someone's Opinion. List of new words. 4 exercises.
Dialogue: ACHIEVING FINANCIAL SUCCESS IN CANADA: A meeting with a financial advisor. Grammar: Double the with Adjectives and Adverbs in the Comparative Degree. Useful Phrases and Models: Importance. Making Enquiries, Asking Questions Politely. Seeking Someone's Opinion, Asking about Views. Expressing an Opinion, Stating Your Views. Opinion Based on Experience. List of new words. 4 exercises.
Dialogue: BUSINESS SITUATION: WEIGHING SKILLS AND TRACK RECORD OF A CANDIDATE FOR PROMOTION. Useful Phrases and Models: Seeking Someone's Opinion, Asking about Views or Impressions. Complimenting on Good Work. Positive Impressions. Polite Reaction to Compliments, Praises. List of new words. 3 exercises.
Dialogue: ASKING FOR HELP IN THE WORKPLACE: A co-worker helping another co-worker. Grammar: get + object / person + infinitive. Useful Phrases and Models: Asking to Explain; Asking for Help. Checking Understanding; Having Trouble Understanding; Asking to Clarify. Confirming Your Understanding. Thanking for Help, Expressing Appreciation; Reaction to Thanks. List of new words. 3 exercises.
Dialogue: DETECTING A MONEY LAUNDERING SCHEME. Grammar: Ellipsis of Infinitives. Useful Phrases and Models: Asking for Instructions. Explaining; Giving Specific, Step-by-step Explanations. Demonstrating. Asking for More Details, Asking to Be More Specific. Asking for Further Information. Satisfaction, Confirmation. List of new words. 3 exercises.
Dialogue: BUSINESS SITUATION: NEGOTIATING A NEW LEASE. Grammar: Correct usage of do and make. Useful Phrases and Models: Asking for Help. Promises to Help, Deal with an Issue. Cautious Opinion. Extensive List of the Most Common do and make Combinations. List of new words. 3 exercises.
Dialogue: A BUSY DAY IN THE OFFICE: Various work-related situations. Useful Phrases and Models: Polite Request. Agreeing to Help; Offering Help. Urgency. List of new words. 3 exercises.
Dialogue: BUSINESS ENVIRONMENT: Clients; Overcoming business challenges. Useful Phrases and Models: Checking Things; Collecting Information. Asking for a Favour; Positive Reaction. Politely Refusing to Help; Inability to Help. Not Giving Up; Insisting, Persuading. Agreeing to Help Without Guaranteeing Positive Results; Uncertain Promises to Help. List of new words. 3 exercises.
Dialogue: DRIVING IN A BIG CITY: Various topics and situations. Grammar: Popular make + object + adjective Combinations. Words and Expressions of Degree. Useful Phrases and Models: Advisability. Criticism, Anger. Useful Words and Combinations: Driving; Turning; Parking; Examples of Bad Driving, etc. List of new words. 3 exercises.
Dialogue: DRIVING TO A CABIN AT A SKI RESORT: Giving driving directions to a friend. Grammar: Differences between farther and further; the farthest and the furthest; between far and a long way; between across and through. Useful Phrases and Models: Distance to Places: Popular Questions and Answers. Approximate Distance to Places. Giving Directions on a Highway. Useful Words: Winter Activities. Driving Conditions. Road Types. List of new words. 3 exercises.
Dialogue: GIVING DRIVING DIRECTIONS TO A LOST MOTORIST; DRIVING ON A HIGHWAY. Grammar: Conjunction once with the Simple Present, the Simple Past and the Simple Present Perfect. Structures of Measuring and Comparing. Useful Phrases and Models: Alternative Suggestions, Ideas. Asking for Driving Directions When You Are Lost. Giving Directions to a Lost Motorist. Time to Places, Approximate Driving Time. List of new words. 3 exercises.
Dialogue: DATING: Discussing people's personalities and behaviour. Useful Phrases and Models: Negative Opinion of People; Negative Personal Qualities. Disliking People. Impressions of People in the Superlative. Unusual Behaviour. Softening a Statement or a Personal Opinion. Looking for an Explanation for Someone's Actions. Trying to Define a Quality. Useful Words and Combinations: Dating, Relationships. Bad Traits of Character. List of new words. 3 exercises.
Elinet's Intermediate Certificate Program covers 100 lessons, each containing five pages of extremely valuable learning materials, including recorded audio dialogues (MP3 files) touching on hundreds of topics and issues. Dialogues are followed by a list of newly introduced words and expressions, carefully selected Useful Phrases and Models, and English Grammar rules, all presented in an easy-to-digest, colourful, attractive manner.