DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation
This work addresses the need for fine-grained evaluation in medical text generation, which is incremental as it builds on existing evaluation methods by adding specific metrics.
The paper tackled the problem of evaluating medical text generation by proposing DocLens, a framework with metrics for completeness, conciseness, and attribution, and demonstrated its effectiveness on three tasks with higher agreement with medical experts than existing metrics.
Medical text generation aims to assist with administrative work and highlight salient information to support decision-making. To reflect the specific requirements of medical text, in this paper, we propose a set of metrics to evaluate the completeness, conciseness, and attribution of the generated text at a fine-grained level. The metrics can be computed by various types of evaluators including instruction-following (both proprietary and open-source) and supervised entailment models. We demonstrate the effectiveness of the resulting framework, DocLens, with three evaluators on three tasks: clinical note generation, radiology report summarization, and patient question summarization. A comprehensive human study shows that DocLens exhibits substantially higher agreement with the judgments of medical experts than existing metrics. The results also highlight the need to improve open-source evaluators and suggest potential directions.