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Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
[article]
2021
arXiv
pre-print
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the task of generating discharge summaries for a clinical encounter. Summaries in this setting need to be faithful, traceable, and scale to multiple long documents, motivating the use of extract-then-abstract summarization cascades. We introduce two new measures, faithfulness and hallucination rate for evaluation in this
arXiv:2104.13498v1
fatcat:mkpw3njbvfcabmsbh2qq5rrl7e