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Automatic summarization of natural language is a widely studied area incomputer science, one that is broadly applicable to anyone who routinely needsto understand large quantities of information. For example, in the medicaldomain, recent developments in deep learning approaches to automaticsummarization have the potential to make health information more easilyaccessible to patients and consumers. However, to evaluate the quality ofautomatically generated summaries of health information, gold-standard, humangenerated summaries are required. Using answers provided by the NationalLibrary of Medicine's consumer health question answering system, we present theMEDIQA Answer Summarization dataset, the first summarization collectioncontaining question-driven summaries of answers to consumer health questions.This dataset can be used to evaluate single or multi-document summariesgenerated by algorithms using extractive or abstractive approaches. In order tobenchmark the dataset, we include results of baseline and state-of-the-art deeplearning summarization models, demonstrating that this dataset can be used toeffectively evaluate question-driven machine-generated summaries and promotefurther machine learning research in medical question answering.

Question-Driven Summarization of Answers to Consumer Health Questions