Topic-Aware Summarization of Lived Health Care Experiences: Large Language Model Evaluation Study

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Storytelling in health care captures emotional and subjective experiences, providing insights into illness and potential behavior change.
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Natural language processing (NLP) techniques can help analyze unstructured spoken-dialogue narratives, overcoming challenges of traditional methods.
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The study focuses on spoken narratives from African American storytellers to identify health care experience themes and gaps.
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Topic detection and summarization were performed using latent Dirichlet analysis and LLMs to enhance clinical interpretability.
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The proposed topic-aware summarization model generates detailed summaries of health care experiences, improving understanding of patient narratives.

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