Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records

Xiangan Liu, Keyang Xu, Pengtao Xie, Eric Xing

Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs). However, the training of a supervised model requires disease-specific medical background and is thus very expensive. We studied how to utilize the intrinsic correlation between multiple EHRs to generate pseudo-labels and train a supervised model with no external annotation. Experiments on real-patient data validate that our model is effective in summarizing crucial disease-specific information for patients.

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