Clinical research text summarization method based on fusion of domain knowledge.

Objective: The objective of this study is to integrate PICO knowledge into the clinical research text summarization process, aiming to enhance the model's comprehension of biomedical texts while capturing crucial content from the perspective of summary readers, ultimately improving the quality of summaries.

Methods: We propose a clinical research text summarization method called DKGE-PEGASUS (Domain-Knowledge and Graph Convolutional Enhanced PEGASUS), which is based on integrating domain knowledge. The model mainly consists of three components: a PICO label prediction module, a text information re-mining unit based on Graph Convolutional Neural Networks (GCN), and a pre-trained summarization model. First, the PICO label prediction module is used to identify PICO elements in clinical research texts while obtaining word embeddings enriched with PICO knowledge. Then, we use GCN to reinforce the encoder of the pre-trained summarization model to achieve deeper text information mining while explicitly injecting PICO knowledge. Finally, the outputs of the PICO label prediction module, the GCN text information re-mining unit, and the encoder of the pre-trained model are fused to produce the final coding results, which are then decoded by the decoder to generate summaries.

Results: Experiments conducted on two datasets, PubMed and CDSR, demonstrated the effectiveness of our method. The Rouge-1 scores achieved were 42.64 and 38.57, respectively. Furthermore, the quality of our summarization results was found to significantly outperform the baseline model in comparisons of summarization results for a segment of biomedical text.

Conclusion: The method proposed in this paper is better equipped to identify critical elements in clinical research texts and produce a higher-quality summary.