  By summarizing longer consumer health questions into shorter and essential
ones, medical question answering (MQA) systems can more accurately understand
consumer intentions and retrieve suitable answers. However, medical question
summarization is very challenging due to obvious distinctions in health trouble
descriptions from patients and doctors. Although existing works have attempted
to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve
the problem, two challenges remain: how to correctly capture question focus to
model its semantic intention, and how to obtain reliable datasets to fairly
evaluate performance. To address these challenges, this paper proposes a novel
medical question summarization framework using entity-driven contrastive
learning (ECL). ECL employs medical entities in frequently asked questions
(FAQs) as focuses and devises an effective mechanism to generate hard negative
samples. This approach forces models to pay attention to the crucial focus
information and generate more ideal question summarization. Additionally, we
find that some MQA datasets suffer from serious data leakage problems, such as
the iCliniq dataset's 33% duplicate rate. To evaluate the related methods
fairly, this paper carefully checks leaked samples to reorganize more
reasonable datasets. Extensive experiments demonstrate that our ECL method
outperforms state-of-the-art methods by accurately capturing question focus and
generating medical question summaries. The code and datasets are available at
https://github.com/yrbobo/MQS-ECL.
