| 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. |
| Outcome: | The proposed system is based on a repository of corpora available for summarization tasks. |
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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. |
| Outcome: | The proposed approach can be used to train abstractive summarization models on large corpora and through a manual evaluation with human annotators. |
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. |
| Outcome: | The proposed ontology covers sample properties, collection methods and distribution, and can be used to streamline future research into a more coherent body of work. |
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. |
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SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
| 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 . |
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| Outcome: | The proposed evaluation metrics are inconsistent with existing evaluation protocols. |
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
A Modular Tool for Automatic Summarization (P19-3)
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| Challenge: | Abstractive automatic summarization methods are supervized, but they require large corpora to perform tasks. |
| Approach: | They propose to use a modular tool for automatic summarization that is as simple as possible for end-users. |
| Outcome: | The proposed tool is open source and written in Java . it could be used as a baseline for future work and evaluate methods on different corpora. |
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation (2020.lrec-1)
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| Challenge: | Summarizing text is not a straightforward task. |
| Approach: | They propose to use automated transcriptions to generate reports from automatic transcriptions as a dataset for neural summarization. |
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A Brief Survey of Textual Dialogue Corpora (2022.lrec-1)
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| Challenge: | Several dialogue corpora are available for research purposes, but they do not cover all the necessities of real-world applications. |
| Approach: | They analyze available dialogue corpora and propose possible approaches to create new ones. |
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Relational Summarization for Corpus Analysis (N18-1)
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| Challenge: | Existing methods for summarizing textual content are often ignored . relationshipal questions are ubiquitous and varied. |
| Approach: | They propose a method which generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
| Outcome: | The proposed method generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
BillSum: A Corpus for Automatic Summarization of US Legislation (D19-54)
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| 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 . |
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