  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.
