mlsanityData to 14 Oct 2026

CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization

arXiv 2206.06581 ↗First posted 14 Jun 2022cs.CLcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 14 Jun 2022.

Builds on

15 of 28 references.

References · 15

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3 more
  1. 2020Question-Driven Summarization of Answers to Consumer Health Questionscited by 39
  2. 2021Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewardscited by 22
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 3; 2025: 2; 2026: 2.

11 papers.