Citance-Contextualized Summarization of Scientific Papers
Shahbaz Syed, Ahmad 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 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.- Anthology ID:
- 2023.findings-emnlp.573
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8551–8568
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.573
- DOI:
- 10.18653/v1/2023.findings-emnlp.573
- Cite (ACL):
- Shahbaz Syed, Ahmad Hakimi, Khalid Al-Khatib, and Martin Potthast. 2023. Citance-Contextualized Summarization of Scientific Papers. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8551–8568, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Citance-Contextualized Summarization of Scientific Papers (Syed et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.findings-emnlp.573.pdf