You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
The General Data Protection Regulation (GDPR) poses enormous challenges on companies and organizations with respect to understanding, implementing, and maintaining the contained constraints. We report on how the ConRelMiner method can be used for untangling the GDPR. For this, the GDPR is filtered and grouped along the roles mentioned by the GDPR and the reduction of sentences to be read by analysts is shown. Moreover, the output of the ConRelMiner - a cluster graph with relations between the sentences - is displayed and interpreted. Overall the goal is to illustrate how the effort for implementing the GDPR can be reduced and a structured and meaningful representation of the relevant GDPR sentences can be found.

Cited Abstract(s):
[1]: Legal texts express conditions in natural language describing what is permitted, forbidden or mandatory in the context they regulate. Despite the numerous approaches tackling the problem of moving from a natural language legal text to the respective set of machine-readable conditions, results are still unsatisfiable and it remains a major open challenge. In this paper, we propose a preliminary approach which combines different Natural Language Processing techniques towards the extraction of rules from legal documents. More precisely, we combine the linguistic information provided by WordNet together with a syntax-based extraction of rules from legal texts, and a logic-based extraction of dependencies between chunks of such texts. Such a combined approach leads to a powerful solution towards the extraction of machine-readable rules from legal documents. We evaluate the proposed approach over the Australian " Telecommunications consumer protections code " .

[2]: This paper addresses the problem of making text mining results more comprehensible to humanities scholars, journalists, intelligence analysts, and other researchers, in order to support the analysis of text collections. Our system, FeatureLens1, visualizes a text collection at several levels of granularity and enables users to explore interesting text patterns. The current implementation focuses on frequent itemsets of n-grams, as they capture the repetition of exact or similar expressions in the collection. Users can find meaningful co-occurrences of text patterns by visualizing them within and across documents in the collection. This also permits users to identify the temporal evolution of usage such as increasing, decreasing or sudden appearance of text patterns. The interface could be used to explore other text features as well. Initial studies suggest that FeatureLens helped a literary scholar and 8 users generate new hypotheses and interesting insights using 2 text collections.

[3]: Implementing rules, constraints, and requirements contained in regulatory documents such as standards or guidelines constitutes a mandatory task for organizations and institutions across several domains. Due to the amount of domain-specific information and actions encoded in these documents, organizations often need to establish cooperations between several departments and consulting experts to guide managers and employees in eliciting compliance requirements. Providing computer-based guidance and support for this often costly and tedious compliance task is the aim of this paper. The presented methodology utilizes well-known text mining techniques and clustering algorithms to classify (families) of documents according to topics and to derive significant sentences which support users in understanding and implementing compliance-related documents. Applying the approach to collections of documents from the security and the medical domain demonstrates that text mining is a promising domain-independent mean to provide support to the understanding, extraction, and analysis of regulatory documents.

[4]: A method and system for detecting an event from a social stream. The method includes the steps of: receiving a social stream from a social network, where the social stream includes at least one object and the object includes a text, sender information of the text, and recipient information of the text; assigning said object to a cluster based on a similarity value between the object and the clusters; monitoring changes in at least one of the clusters; and triggering an alarm when the changes in at least one of the clusters exceed a first threshold value, where at least one of the steps is carried out using a computer device.

[5]: Extracting constraints and process models from natural language text is an ongoing challenge. While the focus of current research is merely on the extraction itself, this paper presents a three step approach to group constraints as well as to detect and display relations between constraints in order to ease their implementation. For this, the approach uses NLP techniques to extract sentences containing constraints, group them by, e.g., stakeholders or topics, and detect redundant, subsuming, and conflicting pairs of constraints. These relations are displayed using network maps. The approach is prototypically implemented and evaluated based on regulatory documents from the financial sector as well as expert interviews.

[6]: Business process modeling has become an important tool for managing organizational change and for capturing requirements of software. A central problem in this area is the fact that the acquisition of as-is models consumes up to 60 of the time spent on process management projects. This is paradox as there are often extensive documentations available in companies, but not in a ready-to-use format. In this paper, we tackle this problem based on an automatic approach to generate BPMN models from natural language text. We combine existing tools from natural language processing in an innovative way and augmented them with a suitable anaphora resolution mechanism. The evaluation of our technique shows that for a set of 47 text-model pairs from industry and textbooks, we are able to generate on average 77 of the models correctly.

[7]: 

[8]: Modeling is an important and time consuming part of the business process management life-cycle. An analyst reviews existing documentation and queries relevant domain experts to construct both mental and concrete models of the domain. To aid this exercise, we propose the Rapid Business Process Discovery (R-BPD) framework and prototype tool that can query heterogeneous information resources (e.g. corporate documentation, web-content, code e.t.c.) and rapidly constructproto-models to be incrementally adjusted to correctness by an analyst. This constitutes a departure from building and constructing models toward just editing them. We believe this rapid mixed-initiative modeling will increase analyst productivity by significant orders of magnitude over traditional approaches. Furthermore, the possibility of using the approach in distributed and real-time settings seems appealing and may help in significantly improving the quality of the models being developed w.r.t. being consistent, complete, and concise.

[9]:
Extracting knowledge from text is a broad and highly regarded task in science and practice. One distinction is the type of text or documents that is analyzed. It ranges from social media texts, e.g., [4] over regulatory documents, e.g., [7] [1] [5] [3] and business process descriptions, e.g., [6] [8] [4]1 to historic text analysis as in digital humanities, e.g., [2] .