  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.
This dataset can be used to evaluate single or multi-document summaries
generated by algorithms using extractive or abstractive approaches. In order to
benchmark the dataset, we include results of baseline and state-of-the-art deep
learning summarization models, demonstrating that this dataset can be used to
effectively evaluate question-driven machine-generated summaries and promote
further machine learning research in medical question answering.
