In doctor-patient conversations, identifying medically relevant information is crucial, posing the need for conversation summarization. In this work, we propose the first deployable real-time speech summarization system for real-world applications in industry, which generates a local summary after every N speech utterances within a conversation and a global summary after the end of a conversation. Our system could enhance user experience from a business standpoint, while also reducing computational costs from a technical perspective. Secondly, we present VietMed-Sum which, to our knowledge, is the first speech summarization dataset for medical conversations. Thirdly, we are the first to utilize LLM and human annotators collaboratively to create gold standard and synthetic summaries for medical conversation summarization. Finally, we present baseline results of state-of-the-art models on VietMed-Sum. All code, data (English-translated and Vietnamese) and models are available online: https://github.com/leduckhai/MultiMed/tree/master/VietMed-Sum

Please cite this paper: https://arxiv.org/abs/2406.15888

@article{VietMed_Sum,
title={Real-time Speech Summarization for Medical Conversations},
author={Le-Duc, Khai and Nguyen, Khai-Nguyen and Vo-Dang, Long and Hy, Truong-Son},
journal={arXiv preprint arXiv:2406.15888},
booktitle={Interspeech 2024},
url = {https://arxiv.org/abs/2406.15888},
year={2024}
}

This model summarizes medical dialogues in Vietnamese. It can work in tandem with an ASR system to provide real-time dialogue summary.

Install the pre-requisite packages in Python.

Use the code below to get started with the model.