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Question-aware transformer models for consumer health question summarization.

Shweta Yadav1, Deepak Gupta2, Asma Ben Abacha3

  • 1University of Illinois Chicago, IL, USA.

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Summary
This summary is machine-generated.

This study introduces a new method for summarizing consumer health questions, making online health information easier to find. The model uses medical terms and question types to create concise summaries, improving search results.

Keywords:
Abstractive SummarizationBiomedical Question AnsweringConsumer Health Question AnsweringConsumer Health Question SummarizationTransformers

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Area of Science:

  • Natural Language Processing
  • Health Informatics
  • Artificial Intelligence

Background:

  • Online health information seeking is common, necessitating efficient question-answering systems.
  • Consumer health questions often contain extraneous information, hindering accurate retrieval.
  • Abstractive summarization can simplify complex questions for better understanding.

Purpose of the Study:

  • To develop an abstractive question summarization model for consumer health questions.
  • To improve the understanding and processing of user queries in health information systems.
  • To generate informative summaries that capture the core intent of health-related questions.

Main Methods:

  • Developed an abstractive summarization model incorporating medical entity recognition.
  • Utilized multiple Cloze tasks to identify and focus on key medical entities.
  • Integrated question-type information into the decoder for type-specific summaries.

Main Results:

  • The proposed framework significantly improved summarization performance on the MeQSum benchmark.
  • Achieved a 10.2 ROUGE-L point improvement over the previous state-of-the-art method.
  • Manual evaluation confirmed the correctness and informativeness of the generated summaries.

Conclusions:

  • Abstractive summarization leveraging medical entity recognition enhances health question understanding.
  • The model effectively identifies and utilizes key information for generating accurate summaries.
  • This approach offers a promising solution for improving online health information retrieval systems.