Citation Sentence Generation Leveraging the Content of Cited Papers

Akito Arita, Hiroaki Sugiyama, Kohji Dohsaka, Rikuto Tanaka, Hirotoshi Taira


Abstract
We address automatic citation sentence generation, which reduces the burden on writing scientific papers. For highly accurate citation senetence generation, appropriate language must be learned using information such as the relationship between the cited source and the cited paper as well as the context in which the paper cited. Although the abstracts of papers have been used for the generation in the past, they often contain extra information in the citation sentence, which might negatively impact the generation of citation sentences. Therefore, this study attempts to learn a highly accurate citation sentence generation model using sentences from cited articles that resemble the previous sentence to the cited location, thereby utilizing information that is more useful for citation sentence generation.
Anthology ID:
2022.sdp-1.19
Volume:
Proceedings of the Third Workshop on Scholarly Document Processing
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard, Lucy Lu Wang
Venue:
sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
170–174
Language:
URL:
https://aclanthology.org/2022.sdp-1.19
DOI:
Bibkey:
Cite (ACL):
Akito Arita, Hiroaki Sugiyama, Kohji Dohsaka, Rikuto Tanaka, and Hirotoshi Taira. 2022. Citation Sentence Generation Leveraging the Content of Cited Papers. In Proceedings of the Third Workshop on Scholarly Document Processing, pages 170–174, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Citation Sentence Generation Leveraging the Content of Cited Papers (Arita et al., sdp 2022)
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PDF:
https://preview.aclanthology.org/naacl24-info/2022.sdp-1.19.pdf