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In medical dialogue summarization, summaries must be coherent and mustcapture all the medically relevant information in the dialogue. However,learning effective models for summarization require large amounts of labeleddata which is especially hard to obtain. We present an algorithm to createsynthetic training data with an explicit focus on capturing medically relevantinformation. We utilize GPT-3 as the backbone of our algorithm and scale 210human labeled examples to yield results comparable to using 6400 human labeledexamples (~30x) leveraging low-shot learning and an ensemble method. Indetailed experiments, we show that this approach produces high quality trainingdata that can further be combined with human labeled data to get summaries thatare strongly preferable to those produced by models trained on human data aloneboth in terms of medical accuracy and coherency.

Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization