  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.
