Does the structure of textual content have an impact on language models for automatic summarization?

Eve Sauvage, Sabrina Campano, Lydia Ouali, Cyril Grouin


Abstract
The processing of long sequences with models remains a subject in its own right, including automatic summary, despite recent improvements. In this work, we present experiments on the automatic summarization of scientific articles using BART models, taking into account textual information coming from distinct passages from the long texts to be summarized. We demonstrate that taking into account document structure improves the performance of state-of-the-art models and approaches the performance of LongFormer on English.
Anthology ID:
2024.acl-srw.25
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Xiyan Fu, Eve Fleisig
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
280–285
Language:
URL:
https://aclanthology.org/2024.acl-srw.25
DOI:
Bibkey:
Cite (ACL):
Eve Sauvage, Sabrina Campano, Lydia Ouali, and Cyril Grouin. 2024. Does the structure of textual content have an impact on language models for automatic summarization?. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 280–285, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Does the structure of textual content have an impact on language models for automatic summarization? (Sauvage et al., ACL 2024)
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PDF:
https://aclanthology.org/2024.acl-srw.25.pdf