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Automatic medical question summarization can significantly help the system tounderstand consumer health questions and retrieve correct answers. The Seq2Seqmodel based on maximum likelihood estimation (MLE) has been applied in thistask, which faces two general problems: the model can not capture well questionfocus and and the traditional MLE strategy lacks the ability to understandsentence-level semantics. To alleviate these problems, we propose a novelquestion focus-driven contrastive learning framework (QFCL). Specially, wepropose an easy and effective approach to generate hard negative samples basedon the question focus, and exploit contrastive learning at both encoder anddecoder to obtain better sentence level representations. On three medicalbenchmark 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 baselineBART model on three datasets respectively. Further human judgement and detailedanalysis prove that our QFCL model learns better sentence representations withthe ability to distinguish different sentence meanings, and generateshigh-quality summaries by capturing question focus.

Focus-Driven Contrastive Learniang for Medical Question Summarization