Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning
<|reference_start|>Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning: Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs). We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text. We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters. We compared GatorTronGPT with a previous solution based on fine-tuning of a widely used T5 model, using a clinical benchmark dataset MTS-DIALOG. The experimental results show that the GatorTronGPT- 20B model achieved the best performance on all evaluation metrics. The proposed solution has a low computing cost as the LLM parameters are not updated during prompt-tuning. This study demonstrates the efficiency of generative clinical LLMs for clinical ATS through prompt tuning.<|reference_end|>
@article{lyu2024automatic,
    title={Automatic Summarization of Doctor-Patient Encounter Dialogues Using
  Large Language Model through Prompt Tuning},
    author={Mengxian Lyu, Cheng Peng, Xiaohan Li, Patrick Balian, Jiang Bian,
  Yonghui Wu},
    journal={arXiv preprint arXiv:2403.13089},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13089},
    primaryClass={cs.CL}
}