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Medical text generation aims to assist with administrative work and highlightsalient information to support decision-making. To reflect the specificrequirements of medical text, in this paper, we propose a set of metrics toevaluate the completeness, conciseness, and attribution of the generated textat a fine-grained level. The metrics can be computed by various types ofevaluators including instruction-following (both proprietary and open-source)and supervised entailment models. We demonstrate the effectiveness of theresulting framework, DocLens, with three evaluators on three tasks: clinicalnote generation, radiology report summarization, and patient questionsummarization. A comprehensive human study shows that DocLens exhibitssubstantially higher agreement with the judgments of medical experts thanexisting metrics. The results also highlight the need to improve open-sourceevaluators and suggest potential directions.

DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation