Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
This addresses the challenge of data scarcity in medical summarization for healthcare applications, though it is incremental as it builds on existing GPT-3 capabilities.
The paper tackled the problem of limited labeled data for medical dialogue summarization by using GPT-3 to generate synthetic training data, achieving results comparable to using 30 times more human-labeled examples (~6400 vs. 210).
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summarization require large amounts of labeled data which is especially hard to obtain. We present an algorithm to create synthetic training data with an explicit focus on capturing medically relevant information. We utilize GPT-3 as the backbone of our algorithm and scale 210 human labeled examples to yield results comparable to using 6400 human labeled examples (~30x) leveraging low-shot learning and an ensemble method. In detailed experiments, we show that this approach produces high quality training data that can further be combined with human labeled data to get summaries that are strongly preferable to those produced by models trained on human data alone both in terms of medical accuracy and coherency.