Citance-Contextualized Summarization of Scientific Papers

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 $textbfWebis-Context-SciSumm-2023$, a new dataset containing 540K~computer science papers and 4.6M~citances therein.

Publication
Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, December 6-10, 2023