%0 Conference Paper %1 syed-etal-2023-citance %A Syed, Shahbaz %A Hakimi, Ahmad %A Al-Khatib, Khalid %A Potthast, Martin %B Findings of the Association for Computational Linguistics: EMNLP 2023 %C Singapore %D 2023 %E Bouamor, Houda %E Pino, Juan %E Bali, Kalika %I Association for Computational Linguistics %K imported %P 8551--8568 %R 10.18653/v1/2023.findings-emnlp.573 %T Citance-Contextualized Summarization of Scientific Papers %U https://aclanthology.org/2023.findings-emnlp.573 %X 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.

@inproceedings{syed-etal-2023-citance, 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.}, added-at = {2024-11-19T15:34:37.000+0100}, address = {Singapore}, author = {Syed, Shahbaz and Hakimi, Ahmad and Al-Khatib, Khalid and Potthast, Martin}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2858bf64b5b67d009120a93abd866bcf2/scadsfct}, booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023}, doi = {10.18653/v1/2023.findings-emnlp.573}, editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika}, interhash = {42a930adbee90f0fe33aca570d73d2ec}, intrahash = {858bf64b5b67d009120a93abd866bcf2}, keywords = {imported}, month = dec, pages = {8551--8568}, publisher = {Association for Computational Linguistics}, timestamp = {2024-11-19T15:34:37.000+0100}, title = {Citance-Contextualized Summarization of Scientific Papers}, url = {https://aclanthology.org/2023.findings-emnlp.573}, year = 2023 }