Challenge: Using news summarization, we aim to target opinionated articles with a well-defined argumentation structure.
Approach: They present a corpus of carefully curated summaries for 266 news editorials.
Outcome: The summarization of opinionated articles with a well-defined argumentation structure is evaluated using a tailored annotation scheme.

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Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking (2023.findings-acl)

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Challenge: Existing summarization models struggle to accurately capture the main arguments of long legal opinions, leading to suboptimal summaries.
Approach: They propose a framework for abstractive summarization of long legal opinions that takes into account the argument structure of the document and reranks them based on alignment with the document's argument structure.
Outcome: The proposed approach outperforms several strong baselines on a dataset of long legal opinions and outperformed existing models.
SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)

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Challenge: a lack of comprehensive studies on evaluation metrics for text summarization hinders progress . a new study aims to improve evaluation metrics that correlate with human judgments .
Approach: They propose to re-evaluate automatic evaluation metrics and share a toolkit for evaluation . they hope to promote a more complete evaluation protocol for text summarization .
Outcome: The proposed evaluation metrics are inconsistent with existing evaluation protocols.
Generating Informative Conclusions for Argumentative Texts (2021.findings-acl)

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Challenge: Argumentative texts often omit explicit conclusions, expecting readers to infer them rather . a corpus of 136,996 arguments is compiled and used to generate informative conclusions .
Approach: They propose to generate informative conclusions from a large-scale corpus of argumentative texts . they propose to use argumentative knowledge to augment the corpus and refine the model .
Outcome: The proposed corpus of argumentative texts and their conclusions is compiled and analyzed . the results show that the proposed model is informative and concise .
An Editorial Network for Enhanced Document Summarization (D19-54)

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Challenge: Existing extractive and abstractive summarization methods are less fluent, coherent and readable, whereas extractive methods are sensitive to vocabulary size, making them more difficult to train and generalize.
Approach: They propose an approach which uses a combination of extractive and abstractive methods to combine a given sequence of sentences into a short version.
Outcome: The proposed method is compared with state-of-the-art methods using extractive-only or abstractive- only baselines.
Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data (L18-1)

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Challenge: Automated summarization has focused on ten to twenty documents, typically news articles, but could in theory analyze hundreds of documents from a wide range of sources and provide an overview to the interested reader.
Approach: They propose a method for creating hierarchical summarization corpora from large, heterogeneous document collections by crowdsourcing relevant content and asking trained annotators to order the relevant information hierarchically.
Outcome: The proposed method can be used to develop and evaluate hierarchical summarization systems.
Summarization of Opinionated Political Documents with Varied Perspectives (2025.coling-main)

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Challenge: Political ideologies can lead people to develop misperceptions of groups with opposing opinions, such as the 2024 US presidential election, French legislative election, or the Brexit referendum.
Approach: They propose a dataset and task for independently summarizing political perspectives in a set of opinionated news articles.
Outcome: The proposed dataset and task evaluates models of varying sizes and architectures on a set of opinionated news articles.
Annotating Arguments in a Corpus of Opinion Articles (2022.lrec-1)

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Challenge: Argument annotation is the process of exposing and justifying one's points of view, with the aim of conveying a logical reasoning through a set of semantically related propositions.
Approach: They propose to use argumentative discourse units to annotate arguments in Portuguese using a multi-layered process to analyze the annotations produced.
Outcome: The proposed model exploits the best practices identified in previous studies while fostering the potential use of the resulting annotated corpus for new purposes.
NEWTS: A Corpus for News Topic-Focused Summarization (2022.findings-acl)

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Challenge: Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or professional content.
Approach: They propose a topical summarization corpus called NEWTS that is annotated via crowd-sourcing.
Outcome: The proposed model can condition summaries on a desired range of themes . the proposed model outperforms Lead-3 baselines on most benchmark datasets .
Live Blog Corpus for Summarization (L18-1)

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Challenge: Live blogs are increasingly popular news format to cover breaking news and live events.
Approach: They propose to collect corpora for automatic live blog summarization by a web-based system . they make the tools publicly available to encourage the research community .
Outcome: The proposed method improves the accuracy of live blog summarization by allowing for public access to the corpus.
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (P19-1)

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Challenge: Existing text summarization datasets are compiled from news articles, where summary-worthy content often appears in the beginning of input articles.
Approach: They present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.
Outcome: The proposed dataset is compared with existing summarization datasets and demonstrates that salient content is evenly distributed in the input.

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