| Challenge: | In the US Congress, over 10,000 bills are introduced each year, with state legislatures introducing tens of thousands of bills. |
| Approach: | They introduce the first dataset for summarizing US Congressional and California state bills . they demonstrate that models built on Congressional bills can be used to summarize California billa . |
| Outcome: | The proposed summarization methods can be applied to states without human-written summaries. |
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| Challenge: | Summarization corpora are numerous but fragmented, making it difficult to pinpoint corporata best suited for a given summarization task. |
| Approach: | They propose a repository containing corpora available to train and evaluate automatic summarization systems. |
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Summarization Beyond News: The Automatically Acquired Fandom Corpora (2020.lrec-1)
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| Challenge: | Abstractive summarization methods require large corpora to train neural architectures. |
| Approach: | They propose a novel automatic corpus construction approach that automatically constructs large open-licensed summarization corpora from existing large text collections and an evaluation process with human annotators. |
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The State and Fate of Summarization Datasets: A Survey (2025.naacl-long)
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| Challenge: | Summarization is the task of shortening a text while preserving the most important information it contains. |
| Approach: | They propose a novel ontology covering sample properties, collection methods and distribution covering sample characteristics, collection method and distribution. |
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CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions (2025.findings-naacl)
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| Challenge: | CaseSumm is a dataset for long-context summarization in the legal domain . human groundtruth summaries are often not available for legal summarizing . |
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BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization (2022.findings-emnlp)
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| Challenge: | Existing text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies and contain strong layout and stylistic biases. |
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Analysis of State-Level Legislative Process in Enhanced Linguistic and Nationwide Network Contexts (2024.naacl-long)
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| Challenge: | a new framework for understanding state-level legislative process improves understanding of state legislation and its implications. |
| Approach: | They propose to use generative large language models to decode legislators' behavior and implications of state policies by establishing a shared nationwide network. |
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Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus (L18-1)
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| Challenge: | Existing datasets for automatic text summarization are small and focused on newswires. |
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MovieSum: An Abstractive Summarization Dataset for Movie Screenplays (2024.findings-acl)
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| Challenge: | Movie screenplay summarization requires an understanding of long input contexts and elements unique to movies. |
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WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation (2021.acl-short)
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| Challenge: | Existing summarization datasets are limited in their ability to evaluate output . a human evaluation is necessary to understand and improve summarizing systems . |
| Approach: | They propose a dataset based on how-to articles and coherent paragraph summaries written in plain language. |
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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. |