# Question-Driven Summarization of Answers to Consumer Health Questions

arXiv: https://arxiv.org/abs/2005.09067 · HTML: https://mlsanity.com/paper/2005.09067/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 30 of 39)

- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
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- [A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks](https://mlsanity.com/paper/2310.04270/) (2023)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [K-QA: A Real-World Medical Q&A Benchmark](https://mlsanity.com/paper/2401.14493/) (2024)
- [Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers](https://mlsanity.com/paper/2306.04504/) (2023)
- [MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension](https://mlsanity.com/paper/2403.08192/) (2024)
- [Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization](https://mlsanity.com/paper/2112.11670/) (2021)
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- [Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions](https://mlsanity.com/paper/2404.09135/) (2024)
- [A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry](https://mlsanity.com/paper/2404.15777/) (2024)
- [Learning to Ask Like a Physician](https://mlsanity.com/paper/2206.02696/) (2022)
- [Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts](https://mlsanity.com/paper/2302.09155/) (2023)
- [Multimodal Large Language Models for Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2504.21051/) (2025)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?](https://mlsanity.com/paper/2306.16931/) (2023)
- [CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization](https://mlsanity.com/paper/2206.06581/) (2022)
- [What Would it Take to get Biomedical QA Systems into Practice?](https://mlsanity.com/paper/2109.10415/) (2021)
- [CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia](https://mlsanity.com/paper/2305.06147/) (2023)
- [No perspective, no perception!! Perspective-aware Healthcare Answer Summarization](https://mlsanity.com/paper/2406.08881/) (2024)
- [Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning](https://mlsanity.com/paper/2505.17436/) (2025)
- [RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering](https://mlsanity.com/paper/2502.13361/) (2025)
- [Script Gap: Evaluating LLM Triage on Indian Languages in Native vs Romanized Scripts in a Real World Setting](https://mlsanity.com/paper/2512.10780/) (2025)
- [Comparative Analysis of Open-Source Language Models in Summarizing Medical Text Data](https://mlsanity.com/paper/2405.16295/) (2024)
- [Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation](https://mlsanity.com/paper/2311.16588/) (2023)
- [Aspect-oriented Consumer Health Answer Summarization](https://mlsanity.com/paper/2405.06295/) (2024)

## Abstract (arXiv)

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.