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.

通过整合源文章中引用的外部论文，本研究旨在提高语言模型在生物医学抽象摘要中的表现。我们提出了一种基于注意力的引文聚合模型，该模型整合了引文论文中的领域特定知识，使神经网络能够利用论文内容和引文论文的相关知识来生成摘要。此外，我们构建并发布了一个大规模的生物医学摘要数据集，为我们的研究奠定了基础。广泛的实验表明，我们的模型优于现有方法，在生物医学抽象文本摘要中取得了显著的改进。