Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures
This addresses the need for automated summarization of medical conversations to aid in decision-making and follow-ups, reducing reliance on costly manual expert summarization.
The paper tackles the problem of summarizing medical dialogues by introducing a novel approach that exploits local structures in patient history gathering, resulting in a model preferred by doctors on twice as many summaries as a baseline and capturing most or all information in 80% of conversations.
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.