DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909333333213184 |
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| author | Xie, Yiqing Zhang, Sheng Cheng, Hao Liu, Pengfei Gero, Zelalem Wong, Cliff Naumann, Tristan Poon, Hoifung Rose, Carolyn |
| author_facet | Xie, Yiqing Zhang, Sheng Cheng, Hao Liu, Pengfei Gero, Zelalem Wong, Cliff Naumann, Tristan Poon, Hoifung Rose, Carolyn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09581 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation Xie, Yiqing Zhang, Sheng Cheng, Hao Liu, Pengfei Gero, Zelalem Wong, Cliff Naumann, Tristan Poon, Hoifung Rose, Carolyn Computation and Language 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. |
| title | DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.09581 |