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.

Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features. SOTA models do not make use of hierarchical representations of discourse structure. In this work, we leverage automatically constructed discourse parse trees within a neural approach and demonstrate a significant improvement on two benchmark entity coreference resolution datasets. We explore how the impact varies depending upon the type of mention.

Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance

**Session Chair: ** Juntao Li 
**Session Volunteer: ** Shayekh Bin Islam

13D: NLP Applications #3

technical paper

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5B-Oral: Discourse and Pragmatics

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance

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