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A Dataset and Benchmark for Consumer Healthcare Question Summarization.

Abhishek Basu1, Deepak Gupta2, Dina Demner-Fushman2

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Summary
This summary is machine-generated.

Summarizing consumer health questions is challenging due to complex language. A new dataset, CHQ-Sum, provides expert-annotated data to improve health information summarization systems.

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Area of Science:

  • Natural Language Processing
  • Health Informatics
  • Information Retrieval

Background:

  • Consumer health questions online are often lengthy and lack key details, complicating information retrieval.
  • Existing summarization datasets do not adequately address the nuances of consumer health queries.
  • Effective summarization is crucial for distilling essential information from vast amounts of online health data.

Purpose of the Study:

  • To introduce a novel dataset specifically designed for consumer health question summarization.
  • To facilitate the development of more accurate and efficient health information summarization systems.
  • To provide a benchmark for evaluating summarization models in the consumer health domain.

Main Methods:

  • A new dataset, CHQ-Sum, was created, containing 1,507 consumer health questions.
  • Each question in the dataset is annotated by domain experts with corresponding summaries.
  • The dataset was derived from community question-answering forums to reflect real-world user queries.

Main Results:

  • The CHQ-Sum dataset comprises 1,507 expert-annotated consumer health questions and summaries.
  • The dataset was benchmarked using several state-of-the-art summarization models.
  • Initial benchmarking demonstrated the dataset's utility for evaluating summarization performance.

Conclusions:

  • The CHQ-Sum dataset addresses the need for specialized resources in consumer health question summarization.
  • This resource is valuable for advancing research in natural language understanding for healthcare.
  • The dataset will aid in developing systems that can better understand and summarize patient-related queries.