On the Summarization of Consumer Health Questions

Asma Ben Abacha, Dina Demner-Fushman


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.
Anthology ID:
P19-1215
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2228–2234
Language:
URL:
https://www.aclweb.org/anthology/P19-1215
DOI:
10.18653/v1/P19-1215
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1215.pdf