CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization
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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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Citing papers per year: 2022: 1; 2023: 3; 2024: 3; 2025: 2; 2026: 2.
11 papers.
- 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 163
- 2022Multi-label Few-shot ICD Coding as Autoregressive Generation with Promptcited by 17
- 2025Multimodal Large Language Models for Medicine: A Comprehensive Surveycited by 16
- 2024MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language Models on Medical Text Summarizationcited by 8
- 2023Medical Question Summarization with Entity-driven Contrastive Learningcited by 5
- 2023A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Modelscited by 3
- 2024The State and Fate of Summarization Datasets: A Surveycited by 3
- 2026Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judgecited by 3
- 2024Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in Finetuning Pretrained Language Modelscited by 1
- 2025Identifying and Addressing User-level Security Concerns in Smart Homes Using "Smaller" LLMscited by 0
- 2026A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLPcited by 0
- 2026A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP8 Apr 2026
- 2026Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge7 Apr 2026
- 2025Identifying and Addressing User-level Security Concerns in Smart Homes Using "Smaller" LLMs23 Sep 2025
- 2025Multimodal Large Language Models for Medicine: A Comprehensive Survey29 Apr 2025
- 2024The State and Fate of Summarization Datasets: A Survey7 Nov 2024
- 2024Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in Finetuning Pretrained Language Models4 Oct 2024
- 2024MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language Models on Medical Text Summarization7 May 2024
- 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics9 Oct 2023
- 2023A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models18 Apr 2023
- 2023Medical Question Summarization with Entity-driven Contrastive Learning15 Apr 2023
- 2022Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt24 Nov 2022