论文标题
使用模块化摘要技术从医生对话中生成肥皂笔记
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
论文作者
论文摘要
每次患者访问后，医生草稿长度半结构化临床摘要称为肥皂笔记。虽然对临床医生和研究人员来说是无价的，但创建数字肥皂笔记却是繁重的，这导致了医师的倦怠。在本文中，我们介绍了第一个完整的管道，以利用深层摘要模型来基于医生与患者之间的对话的转录来生成这些笔记。在探索了整个抽象范围的一系列方法之后，我们提出了cluster2sent，一种算法，即（i）提取与每个摘要相关的重要话语； （ii）集群在一起相关的话语；然后（iii）每个集群生成一个摘要句子。 cluster2sent的表现优于其纯粹的抽象性对应于8个Rouge-1点，并且由专家人类评估者评估，产生了更大的事实和连贯性句子。为了获得可重复性，我们在公开可用的AMI数据集上表现出了类似的好处。我们的结果表达了将摘要汇总到部分的好处，并在构建摘要语料库时注释了支持证据。
Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose Cluster2Sent, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster. Cluster2Sent outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora.
