The growth of online consumer health questions has led to the necessity for
reliable and accurate question answering systems. A recent study showed that
manual summarization of consumer health questions brings significant
improvement in retrieving relevant answers. However, the automatic
summarization of long questions is a challenging task due to the lack of
training data and the complexity of the related subtasks, such as the question
focus and type recognition. In this paper, we introduce a reinforcement
learning-based framework for abstractive question summarization. We propose two
novel rewards obtained from the downstream tasks of (i) question-type
identification and (ii) question-focus recognition to regularize the question
generation model. These rewards ensure the generation of semantically valid
questions and encourage the inclusion of key medical entities/foci in the
question summary. We evaluated our proposed method on two benchmark datasets
and achieved higher performance over state-of-the-art models. The manual
evaluation of the summaries reveals that the generated questions are more
diverse and have fewer factual inconsistencies than the baseline summaries

本文提出了一种基于强化学习的抽象式问题摘要框架，该框架通过 downstream 任务中的问题类型识别和问题重点识别得到两个新颖的奖励来规范问题生成模型，从而确保问题摘要的语义有效并鼓励包含关键的医疗实体 / 重点。作者在两个基准数据集上对其所提出的方法进行了评估，并取得了比现有模型更高的性能。手动评估总结结果表明生成的问题更具多样性且不具备基线总结的不准确性。