In reality, Mackinder's world-island remained a political and economic backwater throughout the twentieth century, but the announcement of China's Belt & Road Initiative (BRI) in 2013 and the return of Russia to great power politics in 2014 brought Eurasia back into fashion. Eurasianism, in one form or another, has become a kind of house ideology of Chinese and Russian strategists. Of course, Chinese and Russian scholars may have no choice but to be enthusiastic about Eurasia, but two new books from Western authors also take the idea very seriously. The Norwegian political scientist Glenn Diesen explores Russia's Eurasian dream in his thoroughly analytical Russia's Geoeconomic Strategy for a Greater Eurasia, while the Portuguese diplomat and banker Bruno Macaes focuses more on the economic pull of China in his half-history, half-travelogue The Dawn of Eurasia: On the Trail of the New World Order. Both books provide eminently readable and thoroughly up-to-date treatments of the meaning of Eurasia today, and both authors buy into Mackinder's basic thesis that Eurasia will "once again" be the geographical pivot of history.
This essay appears in the March Quadrant.
"Once again", because it is a Eurasianist trope that during the era of the great khans, various Mongol hordes sat at the centre of the world-island's (and thus the world's) political economy. This view of history transforms Genghis Khan from the bloodthirsty slaughterer of the steppes into a ruthless but effective unifier of East and West. Eurasianists inevitably invoke the caravan routes of the past, comparing them to the pipeline routes of the present and the rail corridors of the future. Writing in 1904, Mackinder thought that Moscow would soon become the Chicago of a Eurasian rail network; a century later, Diesen seems inclined to the same view, while Macaes seems to think Kazakhstan a more likely nodal point. All Eurasianists tend to forget that seaborne trade is far cheaper than land transportation of any kind, whether camel caravan or rail freight. The camel trains of Eurasian lore were made necessary only by the periodic closure of the (much cheaper) sea routes around India and up the Persian Gulf leading to the short hop overland to Damascus and Constantinople. Once Vasco da Gama rounded the Cape of Good Hope in 1497, the world never looked back.
The Portuguese Macaes should know this, but perhaps the Norwegian Diesen can be forgiven for taking the overland route to Asia. Diesen places twenty-first-century terrestrial infrastructure networks at the centre of his analysis. He finds an intellectual framework for this analysis in the emerging field of geoeconomics (defined by Diesen as "the economics of geopolitics"), and devotes his book to elucidating Russia's optimal geoeconomic strategy for Eurasian dominance.
Geoeconomics is a new discipline, and it has not yet developed a master suite of theories and methods that are accepted as the consensus of its practitioners. Diesen is thus somewhat on his own in laying down the scientific terms of his analyses, and he brilliantly rises to the challenge of theorising political power in geoeconomic terms. Diesen's understanding of geoeconomic power centres on the importance of what he calls "balance of dependence" logic. Diesen sees the balance of dependence as the economic analogue of the balance-of-power logic that prevails in geopolitical theory. Whereas geopolitical power is ordinarily wielded through positive actions like the use of military force, geoeconomic power is wielded by withholding engagement in ways that could harm a counterpart's economy. For example, if Russia and China depend on American interbank payments networks, but the United States does not depend on Russian or Chinese networks, then the United States can wield geoeconomic power by threatening to impose economic sanctions that would in effect exclude Russia or China from the global financial system. In this case, the balance of dependence favours the United States. And in fact this unfavourable balance of dependence has prompted both Russia and China to sponsor independent payments networks that cannot be cut off by US sanctions.
Meanwhile in the balance of dependence between the European Union and Russia, Russia depends on European markets for exports to generate hard currency while the EU depends on Russian oil and (crucially) natural gas to provide for its core energy needs. Here the geoeconomic balance of dependence seems to tilt decisively in Russia's favour, as the 2014 Ukraine crisis amply demonstrated. Although the EU and several of its most powerful member states condemned Russia's annexation of Crimea, dependence on Russian gas deterred European countries from taking any meaningful action in response to the crisis. In theory, the EU could have crippled the Russian economy by placing an embargo on Russian energy, but to do so would have plunged much of Europe into cold and darkness. Fixed pipeline routes ensure that Russia's ability to restrict gas supplies to Europe is a much more powerful geoeconomic tool than Europe's ability to restrict purchases from Russia.
The geoeconomics of Eurasian gas pipelines explains why the United States has consistently pressured Europe to develop liquefied natural gas (LNG) import terminals. Of course the United States wants to sell its own gas to Europe, but European LNG imports could equally well come from Iran, Qatar or even Russia itself. The key geoeconomic difference between ocean-borne LNG and terrestrial pipeline gas is that the former is not subject to balance-of-dependence logic, but the latter is. A plurality of providers ensures that, in extremis, countries can always seek alternative sources of supply for LNG imports. Road, rail and (most of all) pipeline imports, by contrast, are constrained to follow fixed routes.
A country can gain even greater geoeconomic power by developing multiple export routes for strategic commodities for which other countries have only one import route. Thus Russia is eagerly building oil and gas pipelines to China and Japan to complement its existing westward-facing pipeline network. Diesen calls this Russia's "swing" strategy. The swing strategy represents more than simple export market diversification. Diversification would give Russia greater ability to withstand the closure of any one market, since the overall economic impact of the loss of any one export market would be relatively less catastrophic. But mere diversification does not confer serious geoeconomic power. It is the ability to choose whether to send any given quantity of energy to Europe or to Asia that really shifts the geoeconomic balance of dependence. A fully integrated Eurasian energy market centred on Russia would give Russia the power to shift between export destinations—each of them dependent on Russian supplies—with little or no economic loss to itself.
Though Diesen makes much of this possibility, the reality is that Russia's Europe-facing and Asia-facing pipeline networks are separated by thousands of miles, and even if they were connected, it would be wildly uneconomic to maintain sufficient excess capacity to be able to switch supplies between the two networks. Russia has nothing like the American Henry Hub for gas and Cushing Hub for oil, places where gigantic storage facilities are connected to dense pipeline networks leading in every direction to the continent's major consumer markets. And no wonder: the distance between the west Siberian head of Russia's Europe-focused gas network and the east Siberian head of Russia's Asia-focused oil network is some 1000 miles. That's as the crow flies, not as the pipeline wiggles. And in Siberia, the pipeline wiggles a lot: the road distance between the two pipeline heads is nearly twice as long. The two networks might in the future be bridged with a bidirectional switching pipeline, but they will never be integrated in a way that makes the swing strategy viable.
Nevertheless, Diesen astutely applies geopolitical balance-of-dependence logic to Russia's strategy of playing China against Japan in seeking funding for the Eastern Siberia oil pipeline and Power of Siberia gas pipeline. Here Russia was able to extract concessions from both countries as the price of connecting them to Russian resources. China is especially keen to access Russian energy because of its potential to reduce Chinese dependence on seaborne oil and gas imports, which could in theory be choked off by the United States at any time due to American mastery of the high seas. But in dealing with China, Russia is dealing with fire. It may be inconceivable that the European Union would take aggressive action to ensure its energy supplies from Russia, but it is perhaps not inconceivable that, at some point in the middle future, China would act against Russia in Siberia if its core energy supplies were threatened. In ordinary times, geoeconomics trumps geopolitics, but in extraordinary times, geopolitics is more likely to be the ultimate arbiter of a country's fate.
Crossing the continent in the other direction, both Diesen and Macaes see the Caspian Sea as a potential route for the north-south integration of Eurasia, and Macaes even goes one further to assert that "China and India are bound to develop the world's largest trading relationship". Eurasian fantasies like these are fostered by viewing the world in terms of political geography rather than physical geography or human geography. Mackinder of all people, one-time Director of the London School of Economics and President of the Geographical Association, should have known better, but he has seduced generations of later analysts into what might be called a "mappist" view of the world. A simple look at the map shows that the Caspian has the potential to connect northern Iranian ports to Russian Astrakhan and thence via the Volga and Russia's canal network to the entire Russian industrial heartland, as Diesen points out in his analysis of north-south integration. But what does Russia have to export to Iran except missiles and tanks? On the other side of the relationship, what use has Russia for Iranian oil and gas? Iran's largest non-energy export is pistachios.
Macaes has actually sailed on the Caspian Sea, where he reports that the water is not blue or green but "breaks apart in patches of endlessly shifting colours". He describes the Caspian as "more a barrier than a bridge", where there are no passenger ferries and the few cargo ships plying the waters are tramp traders running on no fixed schedule. Searching for the centre of Eurasia, the "middle point on the trade route between Europe and China" where in ancient times adventurous Greek traders met their equally adventurous Chinese and Indian counterparts, Macaes settles instead on the remote Central Asian settlement of Tashkurgan, now a small town on the eastern edge of China's restive Xinjiang province. Tashkurgan is only about twelve miles from the border of Tajikistan, but there is apparently no road over the mountains. So much for east-west integration. Tashkurgan is, however, on the Karakoram Highway running south from China through Pakistan to the Indian Ocean.
The Karakoram Highway is a two-lane road that Chinese Belt & Road engineers are now expanding into a four-lane divided highway that has been under way since the beginning of the decade and is still unfinished. It is a flagship project of China's BRI, and it reeks of mappism. In theory, the highway will give the world's second-largest economy (China) a direct outlet to the Indian Ocean via Pakistan and its Chinese-built port of Gwadar. It will also, in theory, connect China to energy supplies from Iran and markets in India. So good, so far—in theory.
In practice, the Karakoram Highway is literally the only paved road over the mountains that separate China from South Asia. That's one paved road across a Eurasian continental divide that stretches for more than 2000 miles from Afghanistan in the west to Myanmar in the east. And the intercontinental road connections in Afghanistan and Myanmar are meagre, to say the least. In all that stretch (and more) there are no railroads and no pipelines across the continental divide. The Karakoram Highway itself is often closed for months at a time due to snow or rockslides. Even if it does ultimately succeed, it has nowhere to go. India is perpetually cut off from Pakistan due to political hostility, and it is much cheaper to get Iranian oil and gas to Shanghai by sea than overland. Imagine even a single Chinese province of 50 million people sucking its energy supplies through the straw of an avalanche-prone highway across a politically unstable country, and you immediately understand the futility of BRI Eurasian mappism. On a map, the ancient city of Tashkurgan looks like a key meeting place of east and west, north and south. In reality, it is a three-petrol-station town (thank you, Google Maps) in the middle of the middle of nowhere.
Macaes also makes the now-obligatory BRI pilgrimage to Khorgos, the shining new dry port on the border between China and Kazakhstan, or as Macaes would have it, "the border between China and Europe". Like all pilgrims to Khorgos, he reports breathlessly on the machinations of the "three giant yellow cranes, shining in the morning sunlight" that tranship containers from China's standard gauge railways to the former Soviet Union's wide gauge. For all the hoopla around the new rail freight routes now connecting Europe and China, the trains themselves can't actually make the journey because of differences in gauge. The three Khorgos gantry cranes that shift containers from Chinese to former Soviet railways are, for Macaes and other BRI boosters, part of "an ambitious new project to build one of the world's greatest ports". He might as well have visited the corresponding transhipment points—complete with their own giant gantry cranes—on the border between Belarus and Poland. There's the same break in rail gauge there, from Soviet wide gauge back to European standard gauge. But no one touts Belarus as the new economic crossroads of the world merely because you have to change trains there on the way from China to Europe.
Ultimately, Macaes does come down to earth, as when he seemingly accepts the judgment of a German foreign ministry official that the Eurasian dreams of Chinese and Russian geoeconomists are really no more than bread-and-butter politics aimed at "diversifying the economy, increasing options". Macaes fully understands that the Chinese economic commitment to Eurasia is driven by the state and based firmly on strategic considerations, not economic ones. Himself a former diplomat, Macaes thinks the European Union should be less squeamish in playing the great game for the heart of Eurasia; that Europe should, when necessary, play more by the rough-and-tumble rules of China and Russia, since the game can't really be played any other way. Macaes takes it for granted that the game must be played, arguing that the EU "must increase its presence in countries that play the decisive role of gateways and connecting nodes along the new routes linking Europe and Asia". A non-Eurasianist might wonder: why bother?
If Diesen's Russia's Geoeconomic Strategy for a Greater Eurasia is an intellectual tour de force that theorises Eurasia in the hard-nosed terms of analytical social science, Macaes's The Dawn of Eurasia is a magic carpet ride through time and space that uses the legendary past as a jumping-off point for an imaginative future. The two books are complementary, presenting what are in many ways parallel arguments in very different terms and for very different audiences. If they both suffer from the same Eurasianist mappism, they nonetheless both represent the best in class of their own treatments of Eurasianism. You won't find a more incisive analysis of Eurasian geoeconomics than Diesen's Russia's Geoeconomic Strategy, and you won't find a more romantic guide to the Eurasian vision than Macaes in The Dawn of Eurasia.
But for those who like to go back to beginnings, the best place to start the Eurasian journey may be Nikolai Danilevskii's Russia and Europe: The Slavic World's Political and Cultural Relations with the Germanic-Roman West, first published in 1871 but only recently made available to English readers in a translation by Stephen M. Woodburn. It is incredible that a book so important in European thought should not have been translated into English until 2013, and Woodburn—a historian, not a professional translator—is owed a debt of gratitude by all English readers who want to more fully understand the intellectual roots of Russian Eurasianism. Woodburn has given us a finely written and well-annotated edition of Danilevskii's Eurasian masterpiece.
Nikolai Danilevskii (1822–85; also Romanised as Nikolay Danilevsky) was a Russian naturalist and explorer who, in the middle of the nineteenth century, led several expeditions into remote areas of the rapidly expanding Russian empire. In 1869 he published his ideas on Slavic unity and Russia's Eurasian heritage in serial form in an obscure St Petersburg journal; these were collected and published in book form in an 1871 second edition. In his book, Danilevskii emphasised the Eurasian origins of modern Russia, which incorporates many institutional elements of the Mongol hordes that ruled over much of Russia's Slavic population from 1240 through to the early sixteenth century. Danilevskii made much of the fact that the Mongols didn't just rule over Russia; they ruled with Russians, and in negotiated dialogue with Russians, in a relationship that ultimately tilted in the other direction. Even today, the Turkic and Mongolian republics of Central Asia live in a continuously negotiated condominium with Russia and Russians.
Danilevskii maintained that Russia was never part of the "Germanic-Roman West" centred on the early medieval empire of Charlemagne, nor were any of the Slavic peoples, who for him constituted a single "cultural-historical type". Danilevskii thought it a mistake for Russia to play a major role in (Western) European affairs and would have had Russia instead focus on providing leadership to the Slavs. For Danilevskii, as for Mackinder, Eastern Europe was the heartland of Eurasia, but Danilevskii did not share Mackinder's taste for conquest and continental dominance. Danilevskii sought instead the internal development of the Slavic world—though of course under Russian tutelage. Danilevskii imagined that a pan-Slavic union led by Russia would be governed from Constantinople (to be renamed "Tsargrad"). His proposed union would have been led by the Russian Tsar in a personal capacity, not ruled from Moscow as provinces of a Russian empire. But Russia never got to Constantinople, and so never obtained the dominance over Eastern Christianity and access to the Mediterranean dreamt of by Danilevskii and his fellow Slavophiles.
Today's Eurasianists may be more focused on Central Asia than Eastern Europe, but Russia's actual foreign policy objectives continue to focus on Mackinder's Eastern European "heartland". Here Russia seems intent on playing Diesen's "balance of dependence" game, exerting selective pressure on weaker members (and would-be members) of the European Union to adopt policies more favourable to Russia. In doing so, Russia may be making headway, but it certainly isn't making many friends.
Friends for our Russia nowhere arise!
Danilevskii did not offer a source for the couplets, and Woodburn considers it possible that they may have been his own. It is certainly true that Russia suffers more than its fair share of slanders, insults, betrayal, envy and fear. But then again, most other countries have long since given up on dreams of creating a "greater" version of themselves through the subjugation of their neighbours. There have in history been greater Polands, greater Hungarys, and even greater Bulgarias, but these days most countries seem content to live within their own borders.
Salvatore Babones is an associate professor at the University of Sydney and the author of The New Authoritarianism: Trump, Populism, and the Tyranny of Experts, recently published by Polity.
Having been a "military-minded schoolboy"this article was a great answer to many childhood fantasies. Anyone tempted by the Eurasian dream should find your rebuttal points self-evident the moment they are pointed out.
The only thing I would add is to remember that Russia was built on river trade, and the rivers run north-south – trade between Scandinavia and Constantinople was easy, but a Polish scholar was denying the existence of the Urals as late as the 16thC.


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The 18 conference rooms have air conditioning and seat up to 400 guests. The conference rooms are supported by the business centre, with Junction 15 of the M40 motorway is just a few yards away, and Warwick Town Centre just a few minutes drive. Perks at the 4-star Hilton Hotel include LivingWell Health Club. Facilities at the club include an indoor swimming pool and a fully equipped gymnasium. Guest can sweat out the stresses of the working week in the sauna and steam room, before cooling off in pool. Chilled water and fresh towels are provided. The bedrooms are bright and practical with flat-screen TV with movies on-demand. All rooms have a work desk and air conditioning, and some have garden views.
Caffè Cino Lounge and Bar is great place to kick-back and relax with friends and family over a cappuccino and a pastry whilst the hotel's Restaurant 360 serves an international menu in contemporary surroundings. You can even order food to your room.


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These elderly PC's were collected throughout my working life and after I retired, the pace of development has left a lot of machines isolated with Old Dos only O/S's and Early verisons of Windows.
Most of these machines were dead on arrival.
But all are now working and provide access to older software which is now not supported on newer machines. Where do you plug in an old ISA card for testing in a modern PC. The answer is Tough Luck, as the ISA slots have all been designed out.
Tandy 1110 HD ( 20 MB hd ) but floppy drive problems.
Book P.C. v1.0 ( very early 386/25mhz ) Tiny foot print and very compact mini PC.
Without some of these machines, some problems were not possible to solve.
A repair for a friend recently, needed a 64mb Dimm testing but it was an old 5 Volt Dimm. Most modern machines would not support this old standard, but one of my old Pc's did so the Dimm was tested and the problem resolved. ( just for the record that Dimm was for a MAC 5500/225 PPC) There are so many resources on the net for PC products and drivers. I you want to repair an old PC's then.
Start Here at :-"Hand me down PC Page" It is an online book, of about 10 years ago, and a good place to start for information on elderly PC's.


text:
Prime News Ghana
Ghana News Updates
8 fast food joints in Accra to visit
By Maame Aba Afful
When you are extremely tired after a hard days work or just too lazy to go through the stress of preparing a whole meal at home with all the resources it requires, Fast Food joints are your best option.
There are numerous fast food joints in Accra but these 8 really standout for their quality and excellent services.
1. Papaye Fast Food
If you have never tasted Papaye's 'take away', it is likely you are very new to Accra. It is one of the oldest fast food joints in Accra, with their most popular dish being friedrice/rice with grilled chicken. Papaye has varoius branches in the capital and can be found on Oxford street osu, Tema, Tesano Highway, and Spintex Road.
2. KFC Oxford Street
KFC as in the world-renowned KFC has several branches in Accra. Its arrival especially on Oxford Street back in 2011 caused a great deal of commotion, and it has since been serving Ghanaians its crispy, tasty chiken, chips, burger, etc at its numerous branches in the capital.
3. Marwako fast food joint
Marwako, aside serving Ghanaian dishes also serves Lebanese cuisine and is patronized by both tourists and Nationals alike. It is also one of the most popular fast food joints in Accra with four fully operational branches at Spintex, Abelemkpe, Mamobi and Labadi.
4.FRANKIE'S
One of the country's oldest fast food joints serves burgers, pizzas, fries and milkshakes as well as Lebanese fast food at its branches at Oxford Street, Osu, and Accra mall food court.
5.PETER PAN
If you're looking for reasonably priced fried chicken, takeaway sushi, friedrice, or Chicken wrap, then Peter Pan is worth visiting. It has branches at Dzorwulu, A&C Shopping Mall, Tema, and Madina.
6.Gold Coast Kenkey: Enough of the friedrice and chicken, Gold Coast kenkey is one of Accra's favourite fast food joints. They are popularly known for serving the fastest 'well-behaved' Kenkey in the capital. Feel free to stop by anytime.
7. Auntie Muni's Waakye
There's no way we can leave out this popular food joint in Accra. This Waakye joint has risen above the ranks in the Waakye-making industry to qualify in this list. Don't be surprised to meet your favourite Ghanaian celebrity there because it is patronized by many of them, including Former President John Mahama.
8.Eddy's Pizza
To all the pizza lovers in Accra, Eddy's Pizza has been serving quite well. It is one of the most popular Pizza HangOut in Ghana with 8 branches Across Greater Accra.
https://www.primenewsghana.com/lifestyle.html
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A long sleeve bolero by Le Petit Coco, featuring one single button across the neck line.
The item is available in 5 colours: Navy, White, Yellow, Green and Orange.
The yellow bolero was specifically chosen for the baby dress Germana.


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Being pro-police
Post by lovinlife101 » Tue May 19, 2015 10:07 am
A journalist should be objective. He/She should tell a fair and balanced not only in the selection of a story, but in the telling of a story.
I don't see that with Bill Harris.
Look at his bio and the majority of stories he has covered is about police. The stories are pro-police.
http://www.minbcnews.com/about/bio.aspx?id=987
There are bad cops and bad departments too, but you won't find those stories here.
craig11152
Location: Ann Arbor
Re: Being pro-police
Post by craig11152 » Tue May 19, 2015 10:12 am
lovinlife101 wrote: There are bad cops and bad departments too, but you won't find those stories here.
Are you having a hard time finding "bad cop" stories? I can help you out.
I no longer directly engage trolls
I'm saying you won't find stories critical of police or police departments on Bill Harris' page because he won't do them.
Feel free to pitch those kinds of stories his way, but don't expect him to cover them.
His bio shows a clear conflict of interest:
"Bill has also distinguished himself in the area of law enforcement with a rare perspective of someone who has been on "both sides of the fence." While anchoring and reporting for WJRT, Bill also served as a uniformed, reserve deputy with the Genesee County Sheriff's Department's Paramedic Division. He is a recent graduate of the FBI Citizens Academy and has trained with numerous federal, state and local law enforcement agencies.
He continues to work closely with numerous agencies and consulting firms, including the Michigan State Police, as a media consultant and instructor and, this past August, served as the Public Information Officer for Flint's "Back To The Bricks" Law Enforcement Task Force."
So he still "works closely with policy agencies and consulting firms" so much so that he's served as a "media consultant and instructor" and "Public Information Officer for 'Back To The Bricks' Law Enforcement Task Force."
Long story short. He gets paid by police agencies for his services. His wife worked as a police dispatcher. Not only is covering police stories a conflict of interest for him, it's payola.
JackAttack FM
Post by JackAttack FM » Tue May 19, 2015 11:50 am
lovinlife101 wrote: A journalist should be objective. He/She should tell a fair and balanced not only in the selection of a story, but in the telling of a story.
I don't see that with... blah, blah, blah.
Same old shit, nit-pick and bitch about the same old station.
You have some personal beef with WEYI that you've been trying to get everybody else to buy with your trolling.
Now that the station finally has a real owner and a better product you're going to bash on the resume of an anchor that's been in the market for 30 years?
You're reaching.
Nothing new.
A chance for me to ask the professionals.....
when a news anchor, whether its this guy in Flint, or a national guy reads a story from the teleprompter who wrote the words?
a second question, who decides what "news" is worthy news for a 1/2 hour show?
Post by lovinlife101 » Tue May 19, 2015 12:04 pm
Welcome back, Jack "ad hominem" Attack!
Seems like you're having difficulty refuting logic.
When a journalist is getting paid by an organization and then does positive stories on said organization, that's called payola (See George Stephanopoulos).
Feel free to defend this practice.
Post by JackAttack FM » Tue May 19, 2015 12:11 pm
craig11152 wrote: A chance for me to ask the professionals.....
90% of the time its a producer (the person that puts the show together) or a writer. In some shops anchors can also produce and write the newscast but, that's in the smaller shops/markets.
...a second question, who decides what "news" is worthy news for a 1/2 hour show?
Typically the producer. The larger the newsroom the more "collaborative" the effort. Everyone pitches stories at an editorial meeting with producers, managers like executive producers, managing editors, assistant news directors or news directors making the final decision on what stories will air and on what newscasts (4pm, 5pm, 6pm, 7pm, 11pm).
But, like I said, the smaller the operation the more is left up the the newscast producer to decide.
lovinlife101 wrote: Seems like you're having difficulty refuting logic.
No, I'm fine.
Get some harder proof then making assumptions off of station bios. The fact you have to bring in a completely different subject into your argument means you're losing it.
Jack "ad hominem" Attack
You do realize this is what you're doing?... attacking without fact.
It is a fact you bash this station all the time. I see you haven't tried to argue against that.
Location: Waterford, MI
Post by Herm » Tue May 19, 2015 12:53 pm
I've worked extensively with Bill. I would never question his journalistic integrity. Journalism is many things but most importantly, it's about telling the truth. I trust that Bill will ALWAYS tell the truth. If you feel the truth is "pro-police in this situation, then in the words of The Dude... "That's like... your opinion, man".
Post by lovinlife101 » Tue May 19, 2015 1:11 pm
Jack "ad hominem" Attack,
See your first post for the proof you so allegedly desire. Attacking a person rather than the argument is the basis of your conversation.
If you checked Bill's bio page, you'll find 17 of 34 stories are positive pieces on police. Zero stories of any criticism on anything related to police.
He is paid by police departments to work for them, according to his bio.
This is clearly a conflict of interest.
Your claim that I bash this station "all the time" is 100% incorrect. You've made no effort to see my other posts observing the other Flint stations as well as Detroit stations.
Post by craig11152 » Tue May 19, 2015 1:51 pm
lovinlife101 wrote: He is paid by police departments to work for them, according to his bio.
That is not clear to me. The closest I read is "He continues to work closely with numerous agencies and consulting firms, including the Michigan State Police, as a media consultant and instructor and, this past August, served as the Public Information Officer for Flint's "Back To The Bricks" Law Enforcement Task Force."
Those could be paid positions but they could just as easily be volunteer positions. I think its a jump to automatically assume he is paid.
Calvert DeForest
Location: The corner of US-16 and M-78
Post by Calvert DeForest » Tue May 19, 2015 3:52 pm
craig11152 wrote: That is not clear to me. The closest I read is "He continues to work closely with numerous agencies and consulting firms, including the Michigan State Police, as a media consultant and instructor and, this past August, served as the Public Information Officer for Flint's "Back To The Bricks" Law Enforcement Task Force."
A lot of times such positions are non-paid, like being a police-liaison for Neighborhood Watch.
Whatever one's opinion is of Bill Harris, he's been in the game long enough to know where the ethical boundaries are drawn, and I highly doubt he would put his reputation on the line for a few extra bucks and a pat on the back.
YouTube is like a sponge that absorbs ego.
WhatIsNews
Post by WhatIsNews » Sat May 23, 2015 1:00 pm
Bill's done a bunch of positive police stories over the last few years. Is it a conflict of interest? I don't know. I don't think any of the stories I've seen him do qualify as unethical. Media should do good stories about police when it makes sense to do so, and Bill's had a pretty impressive career so he's bought himself some leeway.
If a station as a whole had ONLY done stories that paint police in a positive light, I'd be more concerned... but as someone already pointed out, there's been plenty of coverage about controversial police actions lately.
I just think when there is a good police story to do, they put Bill on it (or Bill volunteers for it)... and Bill finds out about more of them because he has some many contacts with them.
If there's a case (or cases) where there are problems with police and Bill HIDES it, then there's a problem.
Post by lovinlife101 » Sat May 23, 2015 11:06 pm
That's the problem. When he's acting as a public information officer for the police department, as he has in the past, he is speaking on behalf of a police department. Any good public information office will paint the organization it works for in the best light. Therefore, working for the police and covering the police as a journalist is a conflict of interest.
We may never know if something was hidden. When a person is the spokesperson/consultant for the police and a journalist, there's no way to know. There's no check and balance, just one side.


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The scarab beetle book 2
During the 19871988 school year, severus snape sent scarab beetles to poppy pomfrey in a failed attempt to help students afflicted by the sleepwalking curse. I was extremely excited and grateful to be on the receiving end of an early copy, i almost cried and as expected, c. The last three to seven segments of each antenna form flat plates, or lamellae, that can be expanded like a fan see japanese beetle insert or folded together into a club see june beetle photo. From dawn until well after dark, i stripped wood and. Still on the hunt for her missing brother, kayli winchesters life amid the academy team comes to an abrupt halt when she and brandon are kidnapped. The family of scarab beetles falls into the following classifications. Several subfamilies have been elevated to family rank e. Download for offline reading, highlight, bookmark or take notes while you read the academy liar. Liar is the second book in the academy scarab beetle series. Scarab beetles and the people of ancient egypt youtube. Stone, listen to thief, the first book in the academy scarab beetle series. The scarab beetle series this is a new academy series for more mature readers. He was the epitome of patience as he stared fixedly out the front windshield.
Stone, listen to liar, the second book in the academy scarab beetle series. Most adult beetles are nocturnal, although the flower chafers cetoniinae and many leaf chafers rutelinae are active during the day. The scarab beetle book series 6 books all formats kindle edition from book 1. Hard road kayli, raven said to me, his russian accent thick after waking up on the couch. He is also depicted as a scarab in a solar barque held aloft by nun. North american scarab beetles range in length from less than 1 2 in. These were relatively small scarabs typically 2 cm to 3 cm long made from a wide variety of hardstones and faience and are distinguished from other scarabs by having naturalistic carved 3d bases, which often also include an integral suspension loop running widthways. The classification of this family has undergone significant change in recent years. Dont be so quick to do so because the scarabbeetle symbolism offers you some valuable insights in navigating life and achieving the prosperity that you desire. The scarab beetle symbolised the sun because the ancient egyptians saw a likeness between the scarab beetle rolling the dung and the sun god rolling the sun, making it shine on earth. Books 1 4 in the wildly successful and swooninducing academy scarab beetle series, together for the first time. Thriftbooks sells millions of used books at the lowest everyday prices. Etsy is the home to thousands of handmade, vintage, and oneofakind products and gifts related to your search. Scarab beetle antennae naturally curious with mary holland.
The scarab beetle subfamily scarabaeinae consists of species collectively called true dung beetles. Just a new girl and a new slightly older team of academy guys. There is some crossover with the ghost bird books, but this series features a whole new set of characters. A third kind of funerary scarab is the naturalistic scarab. Although many are black, brownish, or drab, many scarabs are colorful, some with iridescent greens and other colors, others looking as if they were covered with shiny enamel paint. We personally assess every book s quality and offer rare, outofprint treasures. Kayli is still uncertain about the boys and academy, untrusting, reckless, and at times just not very nice. The particular species of beetle represented in the numerous ancient egyptian amulets and works of art was commonly the large sacred scarab scarabaeus sacer. Shes had enough of the hustle and is determined to become the good girl. Liar is the second book in the scarab beetle series and at least now i understand the series name.
Scarabs are a mesmerizingly diverse family of beetle found in every part of the world except in the oceans and on antarctica. Evans where we learned adults often fly close to the ground, especially over piles of grass, edges of haystacks, compost piles, manure, and other plant debris. My harem of academy guys keeps growing and i dont feel any guilt about it. However, some may feed on decomposing matter including carrion, decaying fruits and fungi. Sep 11, 2019 the symbol appeared many times in the holy book of the dead. I was late to the party with the scarab beetle series, so i incorrectly assumed that when i purchased 6 books, i purchased the whole series but it became pretty apparent when reading the slow pace of tempest that it wasnt going to be nicely tied up at the end of this book.
Mar 21, 2018 scenes from the mummy the mummy behind the scenes cgi scarab process 1999 brendan fraser movie hd duration. There are about 30,000 scarab species comprising about 10 percent of. The tattoos on his bare arms and stomach shifted and danced on his skin as he stretched and sat up. Mar 03, 2020 scarab beetle, any of approximately 30,000 species of beetles that are compact and heavybodied insects with robustly oval outlines. Christmas beetles picture taken on new year eve the beetles in this family are usually medium to large size, occasionally with bright colour. Scarab beetle audiobooks listen to the full series. Aug 09, 2014 i have been waiting as patiently as possible for the release of liar, the second book in c. The only way she scrapes by is to utilize her one skill. This beetle was associated with the divine manifestation of the early morning sun, khepri, whose name was written with the scarab. The grubs have a distinctive head capsule, and easy to identify legs on the thorax.
After going overboard and nearly drowning, kayli regroups and joins axel, corey, brandon, marc, and raven back on board the lucienda. May 03, 2012 this is the larva of a scarab beetle and it is commonly called a grub. The scarab beetle family is very large, with breathtaking variety and often great beauty. Scarab beetles family scarabaeidae brisbane insects. I purchased this book to read to my six year old daughter. Kayli winchester can finally let go of her thieving past and for once walk on the right side of the law. The scarab beetle was also called the dung beetle because of its practice of rolling a ball of dung across the ground which it then used as a food source. I was hoping for a mythic story that we could integrate into our study of ancient egypt. Though we are uncertain of the exact species, we suspect it might be the larva of a green june beetle, cotinis nitida, which you can find pictured on bugguide.
Stone, listen to thief, the first book in the academy scarab beetle series kayli winchester is a dirtpoor girl living out of a hotel, forced to be the parent for a drunken father and teenage brother who shes desperate to keep in school. A lot of people dismiss the scarabbeetle spirit animal as just a small insect that lacks in character. No matter what youre looking for or where you are in the world, our global marketplace of sellers can help you find unique and affordable options. Five scarab beetles are used in the skelegro healing potion. The cshaped larvae, called grubs, are pale yellow or white. Scarab beetle larvae, called grubs, are cshaped and usually live in the ground, feeding on roots. The academy liar the scarab beetle series volume 2. Download free liar the scarab beetle 2 cl stone liar the scarab beetle 2 cl stone eventually, you will entirely discover a additional experience and achievement. Thief book 1 liar book 2 fake book 3 accessory book 4 hoax book 5 sneak peek. Stone, listen to hoax, the fifth book in the academy scarab beetle series someone tried to kill kayli winchester. Scarab, in ancient egyptian religion, important symbol in the form of the dung beetle scarabaeus sacer, which lays its eggs in dung balls fashioned through rolling. I still see potential in this series, but i am getting frustrated. This beetle was famous for his habit of rolling balls of dung along the ground and depositing them in its burrows.
Stone, listen to liar, the second book in the academy scarab beetle series worse than a thief, is a liar. The bottom is divided into eleven horizontal rows, ten of which are inscribed with book of the dead spell 30b written in neat hieroglyphs that read from right to left. Learn about scarab beetles of the family scarabaeidae. Kayli winchester is a dirtpoor girl living out of a hotel, forced to be the parent for a drunken father and teenage brother who shes desperate to keep in school. Download for offline reading, highlight, bookmark or take notes while you read the academy thief. Hi linda, this is the larva of a scarab beetle and it is commonly called a grub. Id been so worried about getting back to him and letting him know where i was, but the whole time he hadnt been home. Ultrabyeasypeasystore scarab beetle steampunk necklaces classic cyber punk victorian mens womens jewellery chain and pendant copper antique style vintage cosplay 3. The scarab came in the shape of a seal and there were many clay sealings. Most of the beetles of this subfamily feed exclusively on dung. Kayli winchester can finally let go of her thieving past and for once walk on the right side. Scarab beetles archives page 4 of 116 whats that bug. I spent time during the days after returning from florida helping corey, brandon and marc clearing out the inside of a house in downtown charleston, making it habitable for a family to move in.
Dont be so quick to do so because the scarab beetle symbolism offers you some valuable insights in navigating life and achieving the prosperity that you desire. This page contains information and pictures about scarab beetles in family scarabaeidae that we found in the brisbane area, queensland, australia. Liar is book two of the scarab beetle series set in the academy world written by cl stone. The egyptian scarab beetle was used in describing the titles of officials, places, even different deities combined with a prayer like with ra behind there is nothing to fear as a sign of a good omen. They are distinguished from other beetles by their unusual antennae, each of which terminates in three flattened plates that fit together to form a club.
This book also shows her to be judgmental and hypocritical. The beetle was associated closely with the sun god because scarabs roll large balls of dung in which to lay their eggs, a behavior that the egyptians thought resembled the progression of the sun through the sky from east to west. When i got back, wils clothes, school books most of his thingswere gone. Stone kylies brother is missing and she returns back to the academy boys for help. Ironically, the scarab beetle a humble, ugly thing represented god to the egyptians, and it represents god to coelho, as well. Sitting calmly with his dark eyes and short cropped hair, raven was complacent. Dear ab, we identified your bumblebee flower beetle, euphoria inda, in beetles of north america by arthur v. The family scarabaeidae, as currently defined, consists of over 30,000 species of beetles worldwide. Khepri was principally depicted as a scarab beetle, though in some tomb paintings and funerary papyri he is represented as a human male with a scarab as a head, or as a scarab with a male human head emerging from the beetle s shell.
If you havent read book one, thief, go back and do that now. The story revolves around a scarab beetle that saves a prince from being caught in a booby trap. Scarab dream interpretation the meaning of scarab in dream. Paperback order in the next 9 hours 14 minutes and get it by. The egyptians saw the egyptian scarab scarabaeus sacer as a symbol of renewal and rebirth. I have been waiting as patiently as possible for the release of liar, the second book in c. Jan 26, 2012 dr campbell price curator of egyptology and the sudan and dr dmitri logunov curator of arthropods discuss the scarab beetle a. Thief is as amazing as any of the books from the academy but with a different group of guys, a different bird and a whole lot more action compared to the first book from the ghost bird series. Beetles in the family scarabaeidae share several characteristics, including specialized antennae.
Dung beetles can be placed into three structural guilds based on their method of dung processing namely rollers, dwellers and tunnelers dung. The scarabs association with the daily rebirth of the young sun god, khepri, gave the beetle a prominent role in funerary contexts. The gold that the alchemist created from lead is precisely what causes the thieves to believe that santiago is digging for more treasure, and to beat him. Egyptian scarab beetle ancient egyptian symbols egyptian. The scarabs appeared in the city of the dead, hamunaptra, where they were used in the ritual of the homdai, and were worshipped by the ancient egyptians as the bringers of the sun. The scarab beetle spirit animal a guide to meaning and. Scarabs are small, carnivorous insects that eat the flesh of whatever creature they could catch, particularly humans.
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Suitable for small pools, growing up to 2 ft. and happy in up to 4 in of water. A beautiful iris, having vivid magenta coloured flowers, marked with white. The foliage is also an added benefit as it provides a vertical interest over a long period and is suited to both formal and informal settings. They are relatively easy to grow as long as you remember that they don't like drying-out.


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Q: jquery mobile 1.4 + phonegap 3, which button event : click tap vmousedown Hi what are the best practices and fastest events for click events with buttons, links and selects for Phonegap 3.3 and jquery mobile 1.4 ?
Click, tap, vmousedown ?
Thanks

A: If you are working with jQuery Mobile then it is a vclick.
Unlike tap and click it will work on desktop and mobile browsers alike, and unlike click event this one don't suffer from 300ms delay.
Read my other answer to find out more.
$(document).on('vclick', '#someButton', function(){

});

redpajama_set_name: RedPajamaStackExchange


Seattle-based company Blue North has christened F/V Blue North, a state-of-the-art vessel designed to transform the fishing industry by improving conditions for workers, reducing environmental impact and harvesting seafood more humanely.
The new fish harvesting vessel, christened September 9 at the Port of Seattle's Fishermen's Terminal, features a number of novel technologies that aim minimize environmental impact and improve the quality of harvested seafood as well as working conditions for fishermen.
"Blue North is dedicated to sustainable practices, and we spared no expense when it comes to the features we included on the F/V Blue North that reduce environmental impact," said Kenny Downs, president and CEO at Blue North.
On board all water and waste is captured and treated, leaving nothing behind to contaminate the ocean. The vessel's engine-cooling and heat recovery system recycles water used to cool its engines, and repurposes the captured heat for creating potable water, creating hot water and heating the ship. These features mean less fuel is required to heat the vessel and no waste water is put back in the ocean. Engine power is monitored by a smart grid that detects electrical loads and appropriately distributes energy for maximum efficiency. Blue North is equipped with a diesel electric twin propeller dual-azimuth propulsion system and will be one of the first fishing vessels in the U.S. to meet new Tier III emissions standards.
"Not only are these practices more cost-effective, we believe that protecting our resources and environment is critical to the planet and the fishing industry," Downs said.
In order to improve working conditions for the crew, all fishing gear is inside and hauled through an interior moon pool – another first for the U.S. – so the fishermen are no longer exposed to unpredictable weather and associated risks of fishing in the Bering Sea. This also helps extend the time of fishing operations under severe weather conditions and gives high maneuverability and station keeping capabilities, according to the designer. Additionally, the shipbuilder said the vessel has been built with a heavily weighted box keel design to keep weight low, and an antiroll tank to provide a more stable working platform.
Further improving crew conditions, elevator and conveyer systems reduce the work of loading and unloading ship supplies and seafood products, while auto freezers eliminate the manual loading and unloading freezer trays.
Deluxe staterooms are a mixture of private and double rooms, with a private lavatory in each. Workers sharing staterooms are scheduled for different shifts, so that each can rest privately in the room when off duty.
The F/V Blue North also includes an innovative system designed to ensure more humane harvesting of seafood. A hook-and-line fishing system ensures one fish is handled at a time, and fish on the lines are pulled into the moon pool at the center of the boat. Fish are only out of water for a few seconds before being stunned, processed and frozen on board. Hooks are removed after stunning to reduce stress to the fish.
Blue North claims a long history of sustainable practices. The company's primary product, wild Alaska line-caught cod, comes from the first commercial cod fishery to be certified by the Marine Stewardship Council; this cod is designated a "Best Choice" by the Monterey Bay Aquarium's Seafood Watch program. In addition, Blue North is a producer partner of FishWise, a supplier member of FishChoice, and a business collaborator with Monterey Bay Aquarium's Seafood Watch program.


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\section{Introduction}

Abstractive summarization (ABS) has gained overwhelming success owing to a tremendous development of sequence-to-sequence (seq2seq) model and its variants \cite{DBLP:conf/emnlp/RushCW15,Chopra2016Abstractive,paulus2017deep,DBLP:conf/acl/BansalPG18,Gao2019}.
In tandem with seq2seq models, pointer generator was developed by  \citet{DBLP:conf/acl/SeeLM17} as a solution to tackle the rare words and out-of-vocabulary (OOV) problem associated with generative-based models. The idea behind is to use attention as a pointer to determine the probability of generating a word from both a vocabulary distribution and the source text.
Pointer generator networks have also been extensively accepted by the ABS community due to their efficacy with long document summaries \cite{chen2018fast,hsu2018unified}, title summarization \cite{sun2018multi}, etc.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.5\textwidth]{intro_pic_fin.png}
	\caption{``summary1" only copies keyword from the source text, while ``summary2"  generates new concepts to convey the meaning.}
	\label{fig:intro}
\end{figure}

However, the current power of abstractive summarization falls short of their potential. As the example in Figure \ref{fig:intro} shows, a seq2seq model with a pointer mechanism (marked as the direct pointer) is likely to merely copy parts of the original text to form a summary using keywords and phrases, such as ``317 athletes".
Conversely,  a more human-like summary would be based on one's own understanding of the detail in the words, expressed as higher-level concepts drawn from world knowledge\textemdash like using the word ``group" to replace ``athletes and officials".
 This indicates that a good summary should not simply copy original material, it should also generate new and even abstract concepts that reflect high-level semantics.

Therefore, a pointer generator network that solely considers the source material to generate a summary does not adequately satisfy the needs of high-quality  abstractive summarization. We argue that  concepts have a greater ability to  express deeper meanings  than verbatim words.
As such, it is essential to explore the potential of using concepts from world knowledge to assist with abstractive summarization.
Our developed model not only points to informative source texts but also  leverages conceptual words from human knowledge in the summaries it generates.

Hence, in this paper, we propose a novel model based on a concept  pointer generator that  encourages the generation of conceptual and abstract words. As a hidden benefit, the model also alleviates the OOV problems. Our model uses pointer network to capture the salient information from a source text, and then employs another pointer to generalize the detailed words according to their upper level of expressions. Finally, the output is also consistent with language model by the seq2seq generator.
Unique to our concept pointer is a set of concept candidates particular for a word that is drawn from a huge knowledge base. The set of candidates adheres to a concept distribution, where the probability of each concept being generated is linked to how strongly the candidate represents each word.
Moreover, the concept distribution is iteratively  updated to better explain the target word given the context of the source material and inherent semantics in the texts. Hence, the learned concept pointer points to the most suitable and expressive concepts or words. The optimization function is adaptive so as to cater for different datasets with distantly-supervised training. The network is then optimized end-to-end using reinforcement learning, with the distant-supervision strategy as a complement to further improve the summary.

Overall, the contributions of this paper are: 1) a novel concept pointer generator network that leverages context-aware conceptualization and a concept pointer, both of which are jointly integrated into the generator to deliver informative and abstract-oriented summaries; 2) a novel distant supervision training strategy that favors model adaptation and generalization, which results in performance that outperforms the well-accepted evaluation-based reinforcement learning optimization on a test-only dataset in terms of ROUGE metrics; 3) a statistical analysis of quantitative results and human evaluations from comparative experiments with several state-of-the-art models that shows the proposed method provides promising performance.

\section{Related Work}
\label{sec:relatedwork}

Abstractive summarization supposedly digests and understands the source content and, consequently, the generated summaries are typically a reorganization of the wording that sometimes form new sentences.
Historically, abstractive summarization has been performed through rule-based sentence selection \cite{dorr2003hedge}, key information extraction  \cite{DBLP:conf/acl/GenestL11}, syntactic parsing \cite{DBLP:conf/acl/BingLLLGP15} and so on. However, more recently, seq2seq models with attention have played a more dominant role in generating abstractive summaries \cite{DBLP:conf/emnlp/RushCW15,Chopra2016Abstractive,DBLP:conf/conll/NallapatiZSGX16,DBLP:conf/acl/ZhouYWZ17}.  Extensions to the seq2seq approach include an intra-decoder attention \cite{paulus2017deep} and coverage vectors  \cite{DBLP:conf/acl/SeeLM17} to decrease repetition in phrasing.
Copy mechanism \cite{gu2016incorporating}
has been integrated into these models to tackle OOV problem.
\citet{DBLP:conf/aaai/ZhouYWZ18} went on to propose SeqCopyNet  which copies  complete sequences from an input sentence to further maintain the readability of the generated summary.

Pointer mechanism \cite{vinyals2015pointer} has drawn
much attention in text summarization \cite{DBLP:conf/acl/SeeLM17}, because this technique not only provides a potential solution for rare words and OOV  but also  extends abstractive summarization in a flexible way \cite{DBLP:conf/naacl/CelikyilmazBHC18}.
Further, pointer generator models can effectively adaptive to both extractor and abstractor networks \cite{chen2018fast},

and summaries can be generated by incorporating a pointer-generator and multiple relevant tasks \cite{DBLP:conf/acl/BansalPG18}, such as question or entailment generation, or  multiple source texts \cite{sun2018multi}.

However, work particularly targets the problem of the abstraction is rare. Abstract Meaning Representation (AMR) is used to transform a sentence into a concept graph, then merge those similar concept nodes to form a new summary graph \cite{liu2018toward}. Concepts are also incorporated as auxiliary features \cite{guo2017conceptual}.  \citet{DBLP:conf/emnlp/KryscinskiPXS18} and \citet{weber2018controlling} define the number of new $n$-grams  as the primary criteria of abstractiveness. This makes sense in most cases. But, we believe that abstraction means summarizing detailed content with higher-level semantically related concepts, which has motivated the development of the model proposed in this paper.

\section{The Proposed Model}

\begin{figure*}[t]

	\includegraphics[width=0.9\textwidth,right]{framework.png}

	\caption{The architecture of our model. Blue bar represents the attention distribution over the inputs. Purple bar represents the concept distribution over the inputs. Noted that, this distribution can be sparse since not every word has its upper concept. Green bar represents the vocabulary distribution generated from seq2seq component.}
	\label{fig:framework}
\end{figure*}

Neural abstractive summarization can be described as a  generation process where a sequential input is summarized into a shorter sequential output through a neural network.  Suppose that the sequential  input  $\boldsymbol{x} = \lbrace  {x}_1, \dots,  {x}_i, \dots,   {x}_n \rbrace$ is a sequence of  $n$ number of words, and $i$ is the index of the input.  The shorter (i.e., summarized)  sequence of output  is denoted as  $\boldsymbol{y} = \lbrace  {y}_1, \dots,  {y}_t, \dots,   {y}_m \rbrace$ with number of $m$ words, and $t$ indicates a time step.
As Figure \ref{fig:framework} shows, our model consists of two sub-modules \textemdash an encoder-decoder module and the proposed concept pointer generator module.

\subsection{Encoder-Decoder Framework}
This process is formulated as an encoder-decoder framework that
consists of an encoder and an attention-equipped decoder.
We use a two-layer bi-directional LSTM-RNN encoder and  one-layer uni-directional LSTM-RNN decoder  along  with attention mechanism.

Formally, the encoder  produces sequential hidden states as $(\overrightarrow{\boldsymbol{h}}_1, \dots, \overrightarrow{\boldsymbol{h}}_n)$  and $(\overleftarrow{\boldsymbol{h}}_1, \dots, \overleftarrow{\boldsymbol{h}}_n)$  in the corresponding positions, and the  bi-directional ${h}_i = f_{LSTM}({h}_{i-1},{x}_i)$. Each word $x_i$ in the sequence can be   represented as a concatenation of  the bi-directional hidden states, i.e., $\boldsymbol{h}_i = \lbrack \overrightarrow{\boldsymbol{h}}_i, \overleftarrow{\boldsymbol{h}}_i \rbrack$.
The decoder  generates a  target summary from a vocabulary distribution $P_{vocab}(w)$, which is based on a context vector $\boldsymbol{h}_t^*$ through the following  process:
\begin{equation}
\label{eq:outy}
\begin{split}
P_{vocab}(w) & =P(y_t|\boldsymbol{y}_{<t},\boldsymbol{x}; \theta ) \\
 =& \text{sfm}(\boldsymbol{W}_2(\boldsymbol{W}_1[\boldsymbol{s}_t, \boldsymbol{h}^*_t]+\boldsymbol{b}_1)+\boldsymbol{b}_2)
\end{split}
\end{equation}
where  $\boldsymbol{s}_t$ is the hidden state of the decoder at time step $t$ , and $\boldsymbol{h}^*_t$ is the context vector at time step $t$. $\boldsymbol{W}_1, \boldsymbol{W}_2$, $\boldsymbol{b}_1, \boldsymbol{b}_2$ are trainable parameters, and  sfm($\cdot$) is short for softmax function.

The context vector $\boldsymbol{h}^*_t$ is computed by a weighted sum of the hidden  representations of the source text, and the weight is denoted as the attention ${\alpha}_{t,i}$.
\begin{equation}
\begin{split}
\boldsymbol{h}^*_t &= \sum\nolimits_{i=1}^{n}{\alpha}_{t,i}\boldsymbol{h}_i\\
\alpha_{t,i} &= \text{sfm}(\boldsymbol{v}^\top\tanh(\boldsymbol{W}_h \boldsymbol{h}_i + \boldsymbol{W}_s \boldsymbol{s}_t + \boldsymbol{b}))
\end{split}
\label{equ:contex}
\end{equation}
The softmax function  normalizes the vector of a distribution over
the input position, and $\boldsymbol{v}, \boldsymbol{W}_h, \boldsymbol{W}_s, \boldsymbol{b}$ are trainable parameters.

\subsection{Concept Pointer Generator}
 Pointer networks use attention as a pointer to select segments of the input as outputs \cite{vinyals2015pointer}. As such, a pointer network is a suitable mechanism for extracting salient information, while remaining flexible enough to interface with a seq2seq model for generating an abstractive summarization  \cite{DBLP:conf/acl/SeeLM17}. Our proposed model is essentially an upgrade to this configuration that integrates a new concept pointer network within a unified framework.

\subsubsection{Context-aware Conceptualization}
``Understanding" the instances of a word requires a taxonomic knowledge base that relates those words to a concept space.  In our model, we use an $isA$ taxonomy, called the Microsoft Concept Graph\footnote{The Microsoft concept graph was derived from Probase project. The public data can be downloaded via the provided API:  \url{https://concept.research.microsoft.com/Home/API}} \cite{wang2015inference}, to serve this purpose for two reasons \cite{understanding-short-texts}. First, this graph provides a huge concept space with multi-word terms that cover concepts of worldly facts as concepts, instances, relationships, and values\footnote{The current version is mined from billions of web pages, containing 5.3 M unique concepts, 12.5  M unique entities and 85 M $isA$ relations.}.  Second, the relationships between concepts and entities are probabilistic as a measure of how strongly they are related. Moreover, the probabilities are trustworthy given they have been derived from evidence found in billions of webpages, search log data, and other existing taxonomies. Our model is data-driven and, therefore, is more easily adaptable with probabilities. All these characteristics make the Microsoft Concept Graph a suitable choice for our model.
More detailed examples are available in Appendix A.

The concept graph specifies the probability that each instance $x$ belongs to a  concept $c$, $p(c|x)$. Given a word $x$, we have a distribution over a set of related concepts. Yet, this raises the question of how to identify a context-appropriate concept for a word from the distributional set of candidate concepts. For instance, {\em apple} in the context of ``{\em an apple is good for you health}" tends to be associated with the concept of {\em fruit} instead of {\em company}. Formally, given a  word $x_i$ in a training sentence, a set of $k$ concept candidates, $C_i = \{c_i^1, c_i^2,\cdots, c_i^k\}$, is linked to the word from the knowledge base, with distributional  probabilities over the concepts, i.e., $P(C|x_i) =\{ p(c_i^1), p(c_i^2), \cdots, p(c_i^k) \}$. The task is to find the most suitable concept $c_i^j$ to fit the updated context, represented by the  vector $\boldsymbol{h}_t^*$ in Equation (\ref{equ:contex}), at each time step $t$.

In the case of generating summaries given updated contexts,  a weighted update of the distributional concept candidates needs to be  performed. In the model, the updated weight, denoted as $\beta_i^j$, is estimated by a softmax classifier that is jointly conditioned on the hidden representation of the word $\boldsymbol{h}_i$, the context vectors $\boldsymbol{h}^*_t$, and each of concept  vectors:
\begin{equation}
     \beta_i^j = \text{softmax}(\boldsymbol{W}_{h'}[ \boldsymbol{h}_i, \boldsymbol{h}^*_t,  \boldsymbol{c}_i^j])
\end{equation}
where $j\in[1,k]$, $\boldsymbol{W}_{h'}$
is a trainable parameter, and $\boldsymbol{c}_i^j$ is the vector of the  $j$th concept candidate, which is a representation of the input embeddings.

Together with the concept probability from the existing knowledge base $p(c_i^j)$ and the updated weights based on the contexts $\beta_i^j$, a context-aware conceptualized probability of $j$th concept for the word $x_i$,  $P_{i,j}^c$,  is finally estimated as
\begin{equation}
\label{p_ijc}
    P_{i,j}^c = p(c_i^j)+ \gamma \beta_i^j
\end{equation}
where $\gamma$ is a tunable parameter.
Theoretically,
we will end up with a number of $k$ relevant concepts for each word  $C_i = \{c_i^1, \cdots, c_i^k\}$ with a probability distribution over the set, which is learned as $P^c_i = \{ P_{i,1}^c, \cdots ,  P_{i,j}^c, \cdots, P_{i,k}^c\}$.

\subsubsection{Concept Pointer Generator}
The basic pointer generator network contains two sub-modules, one is the pointer network and the other is the generation network. These two sub-modules jointly determine the probabilities of the words in the final generated summary. The generation probability $p_{gen}$  for the generation network \cite{DBLP:conf/acl/SeeLM17} is learned  by
\begin{equation}
    p_{gen} = \sigma(\boldsymbol{W}_{h^*}\boldsymbol{h}^*_t+\boldsymbol{W}_s \boldsymbol{s}_t +\boldsymbol{W}_y\boldsymbol{y}_{t-1}+\boldsymbol{b}_{gen})
\end{equation}
where $\sigma$ is a sigmoid function.

For the pointer network, our model consists of a pointer to the source text and a further concept pointer to the relevant concepts that have arisen from the source content. These two separate pointers are calculated as follows. The first pointer is taken based on the attention distribution $\alpha_{t,i}$ over the source text. The second concept pointer is operated over a concept distribution of the source text that is scaled element-wise by the attention distribution.

To train the model,
given the likelihood of each concept in the current context, the updates could be performed in two ways. In a hard assignment, the concept that receives the highest score would be selected for the update:
 \begin{equation}
\label{equ:max}
 \widehat{P_{i}^c}_\text{argmax} = P_{i,a}^c, \text{where } a = \arg\max_{j} (\beta_i^j)
\end{equation}
where $a$ is the index of maximized generated weight based on the  contexts, and $P_{i,a}^c$ is obtained by Eqs. (\ref{p_ijc}).

In random selection, each of the concept candidates could be trained randomly to update the parameters:

\begin{equation}
\label{equ:random}
    \widehat{P_{i}^c}_\text{random} = P_{i,j}^c \sim P_i^C
\end{equation}
where $j$ represents the selected concept index. Considering the above baseline generation network and both the  pointer networks, our final  output distribution is
\begin{equation}
\begin{split}
    &P_{\text{final}}(w) = p_{gen}P_{vocab}(w)+ (1-p_{gen})\\
    &(\sum\nolimits_{i:w_i=w}\alpha_{t,i}+ \sum\nolimits_{i:w_i=w} \alpha_{t,i}\times\widehat{P_{i}^c})
\end{split}
\end{equation}
where $\widehat{P_i^c}$ can be updated by $\widehat{P_{i}^c}_{\arg\max}$, or $\widehat{P_{i}^c}_\text{random}$. The difference between these two choices is demonstrated  in the  Experiments section.

\subsection{Objective Learning}
\subsubsection{Basic MLE}
The baseline objective is derived by maximizing the likelihood training for the seq2seq generation, given a reference summary $y^* = \{y^*_1, y^*_2,\cdots, y^*_{m'}\}$ for document $x$. The training objective is to
minimize the negative log-likelihood of the target word
sequence:
\begin{equation}
\resizebox{\hsize}{!}{
   $ \mathcal{L}_{MLE} = -\sum_{t = 1}^ {m'} \log P(y_t^*| y_1^*,\cdots, y_{t-1}^*, \boldsymbol{x}) $
    \label{equ:mle}
    }
\end{equation}

\subsubsection{Evaluation based Reinforcement Learning (RL)}
Similar to \citet{paulus2017deep}, policy gradient methods can directly optimize discrete target evaluation metrics, such as ROUGE.
The basic idea is to explore new sequences and compare them to the
best greedy baseline sequence. Once the baseline sequence $\hat{y}$,  or sampled sequence $y^s$, are  generated, they are compared against the reference sequence $y^*$ to compute the rewards $r(\hat{y})$ and $r(y^s)$, respectively. In the RL training stage, two separate output candidates at each time step are produced:  $y^s$ is sampled from the probability distribution $P(y^s_t|y^s_1,\cdots,y^s_{t-1},x)$
, and $\hat{y}$ is the baseline output.
The training objective is then to minimize
\begin{equation}
\resizebox{\hsize}{!}{
    $\mathcal{L}_{RL} = (r(\hat{y})-r(y^s)) \sum_{t = 1}^{m'} \log P(y_t^s|y^s_1,\cdots,y^s_{t-1},x)$
    }
\end{equation}

It is noteworthy that the samples $y^s$ are selected from a wide range of vocabularies extended by all the concept candidates. This strategy ensures that the model learns to generate sequences with  higher rewards by better exploring a set of close concepts.

Thus, the combination of these two objectives yield improved
task-specific scores while catering a better language model:
$
    \mathcal{L}_{final} = \lambda \mathcal{L}_{RL} + (1-\lambda) \mathcal{L}_{MLE}
$,
where $\lambda$ is a soft-switch between the two objectives.  The model  is pre-trained with MLE loss, then switch to the final loss.

\subsubsection{Distant Supervision (DS) for Model Adaption}

Our intuition is that, if the summary-document pairs are dissimilar to the testing set, the model  could be retrained to adapt  to weaken the influence of the dissimilarity on the final loss.
The result would be a training model that better fits the specific testing data. The challenge is that there are no explicit supervision labels to indicate whether the training set is close to the testing set, so a new training paradigm is needed. In answer to this need and also to provide end-to-end functionality in the model, we developed a simple approach for labeling summary-document pairs by calculating the Kullback-Leibler (KL) divergence between each training reference summary and a set of testing documents. In this way, the training pairs are distantly-labelled for training the model.

Specifically, the representations of the reference summaries and the testing set are computed by summing all the involved word embeddings. Given a testing document $x^{\text{d}}_l$, where $l\in [1, N^{\text{d}}]$ and $N^{\text{d}}$ is the size of the testing corpus, the vector-based representation of one document is  $\boldsymbol{x}_l^{\text{d}} = \exp (\sum_{i=1}^{n'}\boldsymbol{x}^{\text{d}}_i$), where $n'$ is the number of document words involved. The reference summary is represented by $\boldsymbol{y^*} =\exp (\sum_{t=1}^{m'}\boldsymbol{y^*_t}$). We  normalize these vectors through a softmax function to cater for KL calculation. The model adaption with the distant labels is defined as:

\begin{equation}
\resizebox{\hsize}{!}{
   $ \mathcal{L}_{DS} = (\pi -\frac{1}{N^{\text{d}}}\sum_{l=1}^{N^{\text{d}}} D_{\text{KL}}(\boldsymbol{y^*},\boldsymbol{x}_l^{\text{d}})) \mathcal{L}_{\text{MLE}}$
    }
\end{equation}
where $D_\text{KL}(.)$ indicates the KL divergence  between $y^*$ and $x_l^{\text{d}}$, and $\pi$ is a constant parameter that is tuned via adaption to the testing set. The divergences are averaged within the testing set, which indicates the overall distances between testing set and each of the reference summary-document pairs.
In this way, the samples in the training corpus are distantly annotated as either relevant or irrelevant for model adaption, noting that the model is pre-trained with the MLE loss before switching to distantly-supervised training.

\section{Experiments}

\begin{table*}[t]
	\centering
	\caption{ROUGE F1 evaluation results on the Gigaword and ROUGE recall on  DUC-2004 test set. The results with $\dag$ mark are taken from the corresponding papers. Underlined scores are the best without additional optimization.  Bold scores are the best between the two optimization strategies. $\star$ mark indicates the improvements from the baselines to the concept pointer are statistically significant using a two-tailed t-test ($p<0.01$).
	}
	\label{tab:test}
	\begin{tabular}{l|ccc|ccc}
		\hline
		Models & \multicolumn{3}{ c }{Gigaword}&\multicolumn{3}{ c }{DUC-2004}\\\cline{2-4} \cline{5-7}
		&RG-1            & RG-2     & RG-L   & RG-1            & RG-2     & RG-L\\
		\hline

		ABS+$^\dag$  \cite{DBLP:conf/emnlp/RushCW15}    & 29.76           & 11.88     & 26.96  & 28.18           & 8.46     & 23.81       \\
		Luong-NMT$^\dag$  \cite{DBLP:conf/emnlp/LuongPM15} & 33.10   & 14.45 	& 30.71   & 28.55           & 8.79 	& 24.43    \\
		RAS-Elman$^\dag$ \cite{Chopra2016Abstractive} & 33.78 & 15.97 & 31.15 & 28.97 & 8.26 & 24.06\\

		lvt5k-lsent$^\dag$ \cite{DBLP:conf/conll/NallapatiZSGX16}  & 35.30 & 16.64 & 32.62 & 28.61    & 9.42     & 25.24\\
		SEASS$^\dag$   \cite{DBLP:conf/acl/ZhouYWZ17}     & 36.15  & 17.54    &33.63        & 29.21  & 9.56     & 25.51\\
		Seq2seq+att	$^\star$(our impl.)   & 33.11 &	14.67 &	31.06  &28.57 &	9.31 &	24.81 \\
	 	Pointer-generator $^\star$(our impl.) \cite{DBLP:conf/acl/SeeLM17} & 35.98 &	15.99&	33.33&28.28 	&10.04 	&25.69\\
	    Pointer-Cov.-Entail.-Quest.$^\dag$ \cite{DBLP:conf/acl/BansalPG18} & 35.98 & 17.76 &	33.63 & - & - & - \\
	    Seq2seq-Sel.-MTL-ERAM$^\dag$ \cite{DBLP:conf/coling/LiZZZ18} & 35.33 & 17.27 & 33.19 & \underline{29.33} & 10.24& 25.24\\
	    CGU$^\dag$  \cite{DBLP:conf/acl/LinSMS18}    &  36.3 & \underline{18.0} & 33.8 & -&-&-\\

    \textbf{Concept pointer}  &	\underline{36.62} &	16.40 &	\underline{33.98} &29.17 &	\underline{10.38} &	\underline{26.34}\\ \hline
        \textbf{Concept pointer+RL} &	\textbf{38.02} & {16.97} &	\textbf{35.43} & 29.34 &	9.84 &	26.60\\
    \textbf{Concept pointer+DS} & {37.01}&\textbf{17.10}&{34.87} & \textbf{30.39} &	\textbf{10.78} & \textbf{27.53}\\
    \hline
	\end{tabular}
	\label{tab:overall}
\end{table*}
\paragraph{Datasets:}
To evaluate the effectiveness of our proposed model, we conducted training and testing on two popular datasets. The first was the English Gigaword Fifth Edition corpus \cite{parker2011english}.
We replicated the pre-processing steps in  \cite{DBLP:conf/emnlp/RushCW15} to obtain the same training/testing data.
After pre-processing, the corpus contained about 3.8M sentence-summary pairs as training set and 189K pairs as the  development set.
Once pairs with empty titles were removed, the testing set numbered 1951 pairs. The second dataset, DUC2004, was only used for testing. This dataset consists of 500 document-headline summary pairs, where each document is paired with four reference summaries written by humans.
\paragraph{Evaluation Metrics:}
We used ROUGE \cite{lin2004rouge} as the evaluation metric, which measures the quality of a summary by computing the overlapping lexical elements between the candidate summary and a reference summary. Following  previous practice, we assessed RG-1 (unigram), RG-2 (bigram) and RG-L (longest common subsequence - LCS).
Noted that the English Gigaword\footnote{The ROUGE evaluation option is, -m -n 2 -s} testing set contains references of different lengths, while the DUC-2004\footnote{The ROUGE evaluation option is, -n 2 -m -b 75 -s} testing set fixes the summary length to 75 bytes.

\paragraph{Training Setups:}
We initialize word embeddings with 128-d vectors  and fine-tune
them during training. Concepts share the same embeddings with the words. The vocabulary size was set to 150k for both the  source and target text. The hidden state size was set to 256.
The vocabulary size is increased from around 602 to 2216 concepts w.r.t  the different number ($k=1,\cdots, 5$) of concept candidates for each word.
Note  that the generated concepts with UNKs were subsequently  deleted.
Our code is available on \url{https://github.com/wprojectsn/codes}, and the vocabularies and candidate concepts are also included.

We trained our models on a single GTX TITAN GPU machine.
We used  the Adagrad optimizer with a batch size of 64 to minimize the loss.
The initial learning rate and the accumulator value were set to 0.15 and 0.1, respectively. We used gradient clipping with a maximum gradient norm of 2.
At the time of decoding, the summaries were produced through a beam search of size 8.
The hyper-parameter settings were $\lambda = 0.99$, $\gamma=0.1$, $\pi = 2.92$ on  DUC-2004 and $\pi =1.68$ on Gigaword. We trained our concept pointer generator for 450k iterations yielded the best performance,  then took the optimization using RL rewards for RG-L at  95K iterations on DUC-2004 and at 50K iterations on Gigaword. We took the distance-supervised training at 5K iterations on DUC-2004 and at 6.5K iterations on Gigaword.
\paragraph{Baselines:}
The following state-of-the-art baselines were used as comparators.
 \textbf{ABS+} \cite{DBLP:conf/emnlp/RushCW15} is a tuned ABS model with additional features.

\textbf{Luong-NMT} \cite{DBLP:conf/emnlp/LuongPM15} is a  two-layer LSTM encoder-decoder.
\textbf{RAS-Elman} \cite{Chopra2016Abstractive} is a convolution encoder and an Elman RNN decoder with attention.
\textbf{Seq2seq+att} is two-layer BiLSTM encoder and one-layer LSTM decoder equipped with attention.
\textbf{lvt5k-lsent} \cite{DBLP:conf/conll/NallapatiZSGX16} uses   temporal attention to keep track of the past attentive weights of the decoder and restrains the repetition in later sequences.
\textbf{SEASS} \cite{DBLP:conf/acl/ZhouYWZ17} includes an additional selective gate to control information flow from the encoder to the decoder.
\textbf{Pointer-generator} \cite{DBLP:conf/acl/SeeLM17}  is an integrated   pointer network and  seq2seq model.
We implemented this baseline without its coverage mechanism since this is not our focus.
Baseline models also include two pointer-generator based extensions \cite{DBLP:conf/acl/BansalPG18,DBLP:conf/coling/LiZZZ18}. \textbf{CGU} \cite{DBLP:conf/acl/LinSMS18} sets a convolutional gated unit and self-attention for global encoding

\section{Results and Analysis}
The following analysis focuses on investigating whether our model is, first, able to generate abstract and new concepts, and, second, how the overall quality performs against the baselines.
\subsection{Quantitative Analysis}

The results are presented in Table \ref{tab:overall}. We observe that our model outperformed all the strong state-of-the-art models on both datasets in all metrics except for RG-2 on Gigaword. In terms of the  pointer generator performance, the improvements made by our concept pointer are statistically significant
($p<0.01$) across all metrics.

\paragraph{OOV and Summary Length:}
\begin{table}[t]
    \centering
    \caption{OOV problem analysis: percentages (\%(NO. UNK/NO. all generated words)) of generating UNK w.r.t the following three models on Gigaword and DUC-2004 datasets.}
    \label{tab:humantest}
    \resizebox{\linewidth}{!}{
    \begin{tabular}{l|c|c}
    \hline
         Method	& Gigaword  & DUC-2004\\
         \hline
         seq2seq+att & 4.02\%(570/14183)	& 2.08\%(85/4079)\\
         Pointer generator &  1.16\%(207/17859)     &	0.31\% (16/5140)    \\
         Concept pointer & 1.12\%(202/17950) &  0.23\%(12/5230)  \\
    \hline
    \end{tabular}
    }
    \label{tab:unk}
\end{table}
 OOV is another major challenge for current abstractive summarization models. Although generating longer summaries or less UNKs is not our focus, our model still showed improvements in this regard (Table \ref{tab:unk}). We counted the number of UNKs and all generated summary words and measured the proportions in both testing sets.
 The OOV percentages dropped  from 4.02\% to 1.12\% on Gigaword and from 2.08\% to 0.23\% on DUC-2004, which demonstrates that our model is effective at alleviating OOV problems. This result also supports the superior of the concept pointer over the baseline pointer generator. From the statistics, we found that the summaries generated by our concept pointer averaged around 10.46 words, while the pointer generator summaries averaged 10.28 words per summary on DUC-2004. This shows the concept pointer is able to capture more salient content by generating relatively longer summaries.

\paragraph{Abstractiveness:}
 \begin{table}[t]
     \centering
     \caption{Abstractiveness: percentage of novel $n$-grams on Gigaword dataset.}
     \label{tab:abstraction}
     \resizebox{\linewidth}{!}{
     \begin{tabular}{l|c|c|c}
     \hline
    	Models &\multicolumn{3}{ c }{Novel $n$-gram (\%)}\\ \cline{2-4}
         &	$1$-gram &	$2$-gram &	$3$-gram\\ \hline
        Pointer generator &	14.3 &	41.9 &	63.4\\
        Concept pointer  & 17.2 &	45.8 &	68.5\\ \hline
        Reference summary &	25.4 &	65.6 &	78.4\\\hline
     \end{tabular}
     }
     \label{tab:abstract}
 \end{table}

 According to \citet{chen2018fast}, abstractiveness scores are  computed as the  percentage of novel $n$-grams in the  generated summaries that are not included in the source documents. As shown in Table \ref{tab:abstract}, compared with human-written summaries which receive the highest novelty in terms of abstractiveness, our concept pointer generator achieves closest  performance with human-written summaries against the baseline. This result demonstrates a further advantage of our model in producing new and abstract concepts.
Our model is designed to improve semantic relevance and promote  higher abstraction. More generated summary examples can be found in Appendix B.

\subsection{Analysis on Training Strategies}

To evaluate the relative impact of each training strategy with the model, we tested different combinations for comparison with each other and against the baselines.

\paragraph{Context-aware Conceptualization:}
To investigate the impact of training with both the number of concepts $k$ and the concept update strategy mentioned in  Eqs.(\ref{equ:max}) and (\ref{equ:random}), we chose a different number of concept candidates, i.e., $k = 1, 2, 3, 4, 5$, to for the context-aware conceptualization update strategy. Performance was fully evaluated with the three ROUGE metrics as shown in Figure  \ref{fig:conceptnumber}.
The results only vary slightly according to the number of concepts with the random selection strategy (Eq.(\ref{equ:random})), as shown in Figure \ref{a1} and \ref{a2}. This indicates that a random strategy is not very sensitive to the number of extracted topics.  This is, in part, because the concept pointer may or may not be able to point to the correct concepts from multiple candidates.
While in Figure \ref{a3} and \ref{a4}, the optimum settings are clearly apparent, i.e., $k=1$ on Gigaword and $k=2$ on DUC-2004.
Overall, the hard assignment strategy (Eq.(\ref{equ:max})) provided the best performance in practical terms, while random selection (Eq.(\ref{equ:random})) performs stably with different settings.

\begin{figure}[t]
    \centering
    \subfigure[Gigaword:  $\widehat{P_i^C}_\text{random}$]
    {\includegraphics[width =3.8cm]{giga-r-eps-converted-to.pdf}\label{a1}}
    \subfigure[DUC-2004:  $\widehat{P_i^C}_\text{random}$]
    {\includegraphics[width =3.8cm]{duc-r-eps-converted-to.pdf}\label{a2}}
    \subfigure[Gigaword:  $\widehat{P_i^C}_{\arg\max}$]
    {\includegraphics[width =3.8cm]{giga-m-eps-converted-to.pdf}\label{a3}}
    \subfigure[DUC-2004:  $\widehat{P_i^C}_{\arg\max}$]
    {\includegraphics[width =3.8cm]{duc-m-eps-converted-to.pdf}\label{a4}}
    \caption{ROUGE metrics on Gigaword and DUC-2004 w.r.t a different number of concept candidates. Updates were conducted by hard assignment $\widehat{P_i^C}_{\arg\max}$ and random selection $\widehat{P_i^C}_\text{random}$. }
    \label{fig:conceptnumber}
\end{figure}

\paragraph{Training with DS vs. RL:}
 As shown in Table \ref{tab:overall}, our model with either a distant supervision strategy (concept pointer+DS) or reinforcement learning (concept pointer+RL) were both superior to the basic concept pointer generator on both datasets. Further, the relative improvement of the concept pointer+DS over the concept pointer+RL ranged from 3.5\% to 9.6\% on DUC-2004 but was inferior to concept pointer+RL on Gigaword. In comparing the results, it is clear that DS training has a noticeable effect when the testing set is substantially semantically different from the training set but provides less improvement than RL when the two are close. From this analysis, we conclude that the DS strategy is better for model adaption with abstractive summarization.

\subsection{Human Evaluations}

\begin{table}[t]
    \centering
    \caption{Human evaluation: scoring of three models in terms of abstraction and overall quality by human evaluators (the higher the better). The score range could be 0-20. $\star$ indicates the improvements from the baselines to the concept pointer are statistically significant.}
    \label{tab:humantest}
    \resizebox{\linewidth}{!}{
    \begin{tabular}{l|c|c}
    \hline
         Method	& abstraction &	overall quality\\
         \hline
         seq2seq+att $\star$ & 5.85	& 5.65\\
         Pointer generator $\star$ &8.95&	8.10\\
         Concept pointer &10.00	&9.60\\
    \hline
    \end{tabular}
    }
\end{table}
To explore the correctness of our model using human judgment, we conducted a manual evaluation with 20 post-graduate volunteers. We primarily used the following criteria to assess the generated summaries: {\em abstraction}, i.e., Are the abstract concepts contained in the summary appropriate?; and {\em overall quality}, i.e., Is the summary readable, informative, relevant, etc.?
To conduct the evaluation, we randomly selected 20 examples from the DUC 2004 testing set and asked the volunteers to subjectively assess the summaries. Each example consisted of an article and three summaries, i.e., a summary by the seq2seq model, the pointer generator model, and our proposed concept pointer model. The volunteers chose the best summaries for each of the articles according to the above criteria (can be multiple choices). Obviously, the summaries were randomly shuffled, and the model used to produce each was unknown to prevent bias. The scores for each model were ranked by how many times the volunteers chose a summary w.r.t each criteria, averaged by the number of participants. The results are presented in Table \ref{tab:humantest}, which show that our model outperformed both the seq2seq model and the pointer generator  \cite{DBLP:conf/acl/SeeLM17} in both criteria.

 As a last step, we manually inspect the summaries generated by our model, and some examples are presented in Appendix B. We found that the summaries were not as abstract as human-written summary would likely be. The overarching tendency of the model is still to copy segments of the source text and rearrange the phrases into a summary. However, the overall approach does produce more high-level concepts with correct relations compared to the baselines, which demonstrates that our solution is a promising research direction to further pursue. Additionally, the generated summaries are long, fluent, and informative.

\section{Conclusion}
This paper presents a novel concept pointer generator model to improve the abstractive summarization model and generate concept-oriented summaries. We also propose a novel distant supervision strategy for model adaption to different datasets. Both empirical and subjective experiments show that our model makes a statistically significant quality improvement over the state-of-the-art baselines on two popular datasets.

\section*{Acknowledgement}
This work was supported by National Natural Science Foundation of China (No.61602036, 61751201), partially supported by the Research Foundation of Beijing Municipal Science \& Technology  Commission (No. Z181100008918002), and partially supported by Ministry of Education China Mobile Research Foundation (No. MCM20170302).

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Q: How to increase file size of Android App .apk I have a simple question.
I want to add a little dummy file to my .apk in order to make it a little bigger, since it only has 300 KB or so and that is too less.
Does someone know how to add a little unneccessary code to increase the size of the .apk?

A: I am curious as to why you need to increase APK file size.
But anyway, to answer your question, why not just slap in some image or sound file in there to artificially increase your file size?

A: This is really strange as usually people look for reducing the size of application. But if it's your requirement then try the following ways.

*

*Fastest way
make a folder(directory) named "raw" under the res folder(directory) and put files of your desired like mp3,images etc, it will be included to the final apk file as raw resource.

*You can also put some png images to drawable folder but not sure it would work in release mode, latest version of android studio may give you error while generating signed apk if the size of files is too large

*(Not Recommended) Increase your code base like increasing size of java files, adding more number of lines to your xml files etc.
Note -- If you will put your raw files to somewhere else location then whether android studio will not include in the final apk or it will give error respectively.

A: I don't understand why you want to do that...
But you can add files in your res/assets folder

A: Do not add files just to increase size
You are producing this app to be used on people's devices. People have limited resources. If it gets installed 1 million times, think of the total extra network bandwidth to download, total storage space people can't use to store their photos, and electrical power for those steps.
People will not notice that an app is tiny and decide not to use it. They will; however, uninstall the apps taking the most space first or not install in the first place.
While the other answers provide the correct direct answer, I believe they are all incorrect in practice.