Improving Biomedical Abstractive Summarisation with Knowledge
  Aggregation from Citation Papers
Chen Tang Shun Wang Tomas Goldsack Chenghua Lin
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.