Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes

• URL: http://arxiv.org/abs/2104.13498v1
• Date: Tue, 27 Apr 2021 22:45:54 GMT
• Title: Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
• Authors: Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani, Feng Nan, Philip Resnik, Douglas Oard and Parminder Bhatia
• Abstract summary: This paper introduces the task of generating discharge summaries for a clinical encounter. We introduce two new measures, faithfulness and hallucination rate for evaluation. Results across seven medical sections and five models show that a summarization architecture that supports traceability yields promising results.
• Score: 15.689048077818324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 task, which complement existing measures for fluency and informativeness. Results across seven medical sections and five models show that a summarization architecture that supports traceability yields promising results, and that a sentence-rewriting approach performs consistently on the measure used for faithfulness (faithfulness-adjusted $F_3$) over a diverse range of generated sections.

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