Summarizing Doctor’s Diagnoses and Suggestions from Medical Dialogues

Nowadays, doctors can provide consultation services to patients by dialogues on the online medical platforms, and need to summarize their diagnoses and suggestions according to the regulations of the platform, which will play an important guiding role in the follow-up treatments. The essential challenges of automatic summarization lie in the high overlap between summaries and doctors’ original utterances and the adaption to the specific structure of medical dialogues, which are overlooked by the majority of existing work. In response to this problem, we propose a pointer generator network model, dubbed as PMDS, to generate accurate and concise summaries for doctors’ diagnoses and suggestions. PMDS takes the pointer generator network as the basic architecture and uses multi-level enhanced input feature representation, the latter of which helps to effectively distinguish speakers and better focus on key information. We evaluate our proposed model on a Chinese medical dialogue summarization dataset, and the experimental results exceeded several strong baselines in previous studies.