Understanding a medical conversation between a patient and a physician poses
a 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.

本文提出一种基于指针生成网络的医疗对话摘要方法，该方法对医疗本体的使用进行了优化，能够很好地捕捉患者病史中的本地结构，获得了医生的青睐，是一种替代手动摘要方法的现实选择。