Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures.

Anirudh Joshi, Namit Katariya, Xavier Amatriain, Anitha Kannan


Abstract
Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge. Summarization of medical conversations is a particularly important aspect of medical conversation understanding since it addresses a very real need in medical practice: capturing the most important aspects of a medical encounter so that they can be used for medical decision making and subsequent follow ups. In this paper we present a novel approach to medical conversation summarization that leverages the unique and independent local structures created when gathering a patient’s medical history. Our approach is a variation of the pointer generator network where we introduce a penalty on the generator distribution, and we explicitly model negations. The model also captures important properties of medical conversations such as medical knowledge coming from standardized medical ontologies better than when those concepts are introduced explicitly. Through evaluation by doctors, we show that our approach is preferred on twice the number of summaries to the baseline pointer generator model and captures most or all of the information in 80% of the conversations making it a realistic alternative to costly manual summarization by medical experts.
Anthology ID:
2020.findings-emnlp.335
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3755–3763
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.335
DOI:
10.18653/v1/2020.findings-emnlp.335
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.335.pdf
Optional supplementary material:
 2020.findings-emnlp.335.OptionalSupplementaryMaterial.pdf