EMNLP Findings2023
Citance-Contextualized Summarization of Scientific Papers
Shahbaz Syed, Ahmad Dawar Hakimi, Khalid Al Khatib, Martin Potthast
Abstract
Current approaches to automatic summarization of scientific papers generate informative summaries in the form of abstracts. However, abstracts are not intended to show the relationship between a paper and the references cited in it. We propose a new contextualized summarization approach that can generate an informative summary conditioned on a given sentence containing the citation of a reference (a so-called "citance"). This summary outlines the content of the cited paper relevant to the citation location. Thus, our approach extracts and models the citances of a paper, retrieves relevant passages from cited papers, and generates abstractive summaries tailored to each citance. We evaluate our approach using WEBIS-CONTEXT-SCISUMM-2023, a new dataset containing 540K computer science papers and 4.6M citances therein. 1 2 * Equal contribution.
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Builds on5
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- S2ORC: The Semantic Scholar Open Research CorpusKyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kinney et al.ACL 2020 · 424 citations
- TLDR: Extreme Summarization of Scientific DocumentsIsabel Cachola, Kyle Lo, Arman Cohan, Daniel S. WeldEMNLP Findings 2020 · 170 citations
- CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited SupervisionYuning Mao, Ming Zhong, Jiawei HanEMNLP 2022 · 11 citations
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