Unsupervised Pseudo Labeling for Extractive Summarization on Electronic Health Records
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.
ArXiv