Improving Factuality in Clinical Abstractive Multi-Document Summarization by Guided Continued Pre-training

Ahmed Elhady, Khaled Elsayed, Eneko Agirre, Mikel Artetxe

Abstract
Factual accuracy is an important property of neural abstractive summarization models, especially in fact-critical domains such as the clinical literature. In this work, we introduce a guided continued pre-training stage for encoder-decoder models that improves their understanding of the factual attributes of documents, which is followed by supervised fine-tuning on summarization. Our approach extends the pre-training recipe of BART to incorporate 3 additional objectives based on PICO spans, which capture the population, intervention, comparison, and outcomes related to a clinical study. Experiments on multi-document summarization in the clinical domain demonstrate that our approach is competitive with prior work, improving the quality and factuality of the summaries and achieving the best-published results in factual accuracy on the MSLR task.
Anthology ID:
2024.naacl-short.66
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
755–761
Language:
URL:
https://aclanthology.org/2024.naacl-short.66
DOI:
Bibkey:
Cite (ACL):
Ahmed Elhady, Khaled Elsayed, Eneko Agirre, and Mikel Artetxe. 2024. Improving Factuality in Clinical Abstractive Multi-Document Summarization by Guided Continued Pre-training. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 755–761, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Improving Factuality in Clinical Abstractive Multi-Document Summarization by Guided Continued Pre-training (Elhady et al., NAACL 2024)
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