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.
BibTeX
@inproceedings{elhady-etal-2024-improving,
title = "Improving Factuality in Clinical Abstractive Multi-Document Summarization by Guided Continued Pre-training",
author = "Elhady, Ahmed and
Elsayed, Khaled and
Agirre, Eneko and
Artetxe, Mikel",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-short.66/",
doi = "10.18653/v1/2024.naacl-short.66",
pages = "755--761"
}