Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers
This addresses the challenge for biomedical researchers and practitioners who need accurate summaries, but it is incremental as it builds on existing attention-based methods.
The paper tackled the problem of generating biomedical abstractive summaries by aggregating knowledge from citation papers, resulting in a model that outperforms state-of-the-art approaches with substantial improvements.
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.