Abstract
Question understanding is one of the main
challenges in question answering. In real
world applications, users often submit natural language questions that are longer than
needed and include peripheral information that
increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural
abstractive models for medical question summarization. We introduce the MeQSum corpus
of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from
question datasets improves the overall performance, and that pointer-generator networks
outperform sequence-to-sequence attentional
models on this task, with a ROUGE-1 score of
44.16%. We also present a detailed error analysis and discuss directions for improvement
that are specific to question summarization