医疗对话的实时语音摘要
Real-time Speech Summarization for Medical Conversations
AI总结：
本研究提出了首个可部署的实时医疗对话语音摘要系统，并发布了首个医疗对话语音摘要数据集VietMed-Sum，同时利用LLM与人工协同创建摘要并提供了基线结果。
摘要：
在医患对话中，识别相关医疗信息至关重要，因此需要对对话进行摘要。在本研究中，我们提出了首个可部署的、面向工业界实际应用的实时语音摘要系统，该系统在对话内每N个语音话语后生成局部摘要，并在对话结束后生成全局摘要。从商业角度来看，该系统能提升用户体验；从技术角度来看，它能降低计算成本。其次，我们提出了VietMed-Sum，据我们所知，这是首个医疗对话语音摘要数据集。第三，我们首次利用LLM和人工标注员协同创建医疗对话摘要的金标准和合成摘要。最后，我们展示了最先进模型在VietMed-Sum上的基线结果。所有代码、数据（英文翻译和越南语）和模型均在线可用：https://github.com/leduckhai/MultiMed/tree/master/VietMed-Sum
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
