  The quest for seeking health information has swamped the web with consumers'
health-related questions. Generally, consumers use overly descriptive and
peripheral information to express their medical condition or other healthcare
needs, contributing to the challenges of natural language understanding. One
way to address this challenge is to summarize the questions and distill the key
information of the original question. To address this issue, we introduce a new
dataset, CHQ-Summ that contains 1507 domain-expert annotated consumer health
questions and corresponding summaries. The dataset is derived from the
community question-answering forum and therefore provides a valuable resource
for understanding consumer health-related posts on social media. We benchmark
the dataset on multiple state-of-the-art summarization models to show the
effectiveness of the dataset.
