  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.
