  Patient summarization is essential for clinicians to provide coordinated care
and practice effective communication. Automated summarization has the potential
to save time, standardize notes, aid clinical decision making, and reduce
medical errors. Here we provide an upper bound on extractive summarization of
discharge notes and develop an LSTM model to sequentially label topics of
history of present illness notes. We achieve an F1 score of 0.876, which
indicates that this model can be employed to create a dataset for evaluation of
extractive summarization methods.
