Focus-Driven Contrastive Learniang for Medical Question Summarization
This work addresses the challenge of summarizing consumer health questions for better answer retrieval in medical systems, representing an incremental improvement with specific gains.
The authors tackled the problem of automatic medical question summarization by proposing a question focus-driven contrastive learning framework (QFCL) to improve capture of question focus and sentence-level semantics, achieving state-of-the-art results with performance gains of 5.33, 12.85, and 3.81 points over baseline BART on three datasets.
Automatic medical question summarization can significantly help the system to understand consumer health questions and retrieve correct answers. The Seq2Seq model based on maximum likelihood estimation (MLE) has been applied in this task, which faces two general problems: the model can not capture well question focus and and the traditional MLE strategy lacks the ability to understand sentence-level semantics. To alleviate these problems, we propose a novel question focus-driven contrastive learning framework (QFCL). Specially, we propose an easy and effective approach to generate hard negative samples based on the question focus, and exploit contrastive learning at both encoder and decoder to obtain better sentence level representations. On three medical benchmark datasets, our proposed model achieves new state-of-the-art results, and obtains a performance gain of 5.33, 12.85 and 3.81 points over the baseline BART model on three datasets respectively. Further human judgement and detailed analysis prove that our QFCL model learns better sentence representations with the ability to distinguish different sentence meanings, and generates high-quality summaries by capturing question focus.