Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers

• URL: http://arxiv.org/abs/2310.15684v1
• Date: Tue, 24 Oct 2023 09:56:46 GMT
• Title: Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers
• Authors: Chen Tang, Shun Wang, Tomas Goldsack and Chenghua Lin
• Abstract summary: Existing language models struggle to generate technical summaries that are on par with those produced by biomedical experts. We propose a novel attention-based citation aggregation model that integrates domain-specific knowledge from citation papers. Our model outperforms state-of-the-art approaches and achieves substantial improvements in abstractive biomedical text summarisation.
• Score: 24.481854035628434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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