2020

Question-Driven Summarization of Answers to Consumer Health Questions

Savery, Max, Abacha, Asma Ben, Gayen, Soumya et al.

Understand

Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who routinely needs to understand large quantities of information.

For example, in the medical domain, recent developments in deep learning approaches to automatic summarization have the potential to make health information more easily accessible to patients and consumers.
However, to evaluate the quality of automatically generated summaries of health information, gold-standard, human generated summaries are required.
Using answers provided by the National Library of Medicine's consumer health question answering system, we present the MEDIQA Answer Summarization dataset, the first summarization collection containing question-driven summaries of answers to consumer health questions.

Built on

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Question-driven summarization of answers to consumer health questions https://doi.org/10.17605/OSF.IO/FYG46 (2020)
Savery, M., Ben Abacha, A., Gayen, S. & Demner-Fushman, D · 2020
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