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Citance-Contextualized Summarization of Scientific Papers

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023
4 Figures
Bibliography:3 Pages
10 Tables
Appendix:15 Pages
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\textbf{Webis-Context-SciSumm-2023}, a new dataset containing 540K~computer science papers and 4.6M~citances therein.

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