Searching for health information online is becoming customary for more and
more consumers every day, which makes the need for efficient and reliable
question answering systems more pressing. An important contributor to the
success rates of these systems is their ability to fully understand the
consumers' questions. However, these questions are frequently longer than
needed and mention peripheral information that is not useful in finding
relevant answers. Question summarization is one of the potential solutions to
simplifying long and complex consumer questions before attempting to find an
answer. In this paper, we study the task of abstractive summarization for
real-world consumer health questions. We develop an abstractive question
summarization model that leverages the semantic interpretation of a question
via recognition of medical entities, which enables the generation of
informative summaries. Towards this, we propose multiple Cloze tasks (i.e. the
task of filing missing words in a given context) to identify the key medical
entities that enforce the model to have better coverage in question-focus
recognition. Additionally, we infuse the decoder inputs with question-type
information to generate question-type driven summaries. When evaluated on the
MeQSum benchmark corpus, our framework outperformed the state-of-the-art method
by 10.2 ROUGE-L points. We also conducted a manual evaluation to assess the
correctness of the generated summaries.

本文研究针对真实世界的消费者健康问题的抽象总结任务，开发了一个抽象问题总结模型，利用医疗实体的语义识别，借此提高了问题聚焦的涵盖范围和生成信息摘要的能力。同时，我们还将问题类型信息融入到解码器的输入中，实现了问题类型驱动的摘要生成，并在 MeQSum 基准语料库上进行了评估，该框架超过了最先进方法 10.2 ROUGE-L 分，并进行手动评估以评估所生成的摘要的正确性。