Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers

Chen Tang, Shun Wang, Tomas Goldsack, Chenghua Lin


Abstract
Abstracts derived from biomedical literature possess distinct domain-specific characteristics, including specialised writing styles and biomedical terminologies, which necessitate a deep understanding of the related literature. As a result, existing language models struggle to generate technical summaries that are on par with those produced by biomedical experts, given the absence of domain-specific background knowledge. This paper aims to enhance the performance of language models in biomedical abstractive summarisation by aggregating knowledge from external papers cited within the source article. We propose a novel attention-based citation aggregation model that integrates domain-specific knowledge from citation papers, allowing neural networks to generate summaries by leveraging both the paper content and relevant knowledge from citation papers. Furthermore, we construct and release a large-scale biomedical summarisation dataset that serves as a foundation for our research. Extensive experiments demonstrate that our model outperforms state-of-the-art approaches and achieves substantial improvements in abstractive biomedical text summarisation.
Anthology ID:
2023.emnlp-main.40
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
606–618
Language:
URL:
https://aclanthology.org/2023.emnlp-main.40
DOI:
10.18653/v1/2023.emnlp-main.40
Bibkey:
Cite (ACL):
Chen Tang, Shun Wang, Tomas Goldsack, and Chenghua Lin. 2023. Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 606–618, Singapore. Association for Computational Linguistics.
Cite (Informal):
Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers (Tang et al., EMNLP 2023)
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https://aclanthology.org/2023.emnlp-main.40.pdf
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