Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose
| Challenge: | Medical text generation systems are widely used to assist with administrative work and highlight salient information to support decision-making. |
| Approach: | They propose a set of metrics to evaluate completeness, conciseness, and attribution of medical text at a fine-grained level. |
| Outcome: | The proposed framework exhibits substantially higher agreement with medical experts than existing metrics. |
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| Challenge: | Recent studies show that doctors can save significant amounts of time when using automatic note generation. |
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Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations (2023.acl-long)
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Lucy Lu Wang, Yulia Otmakhova, Jay DeYoung, Thinh Hung Truong, Bailey Kuehl, Erin Bransom, Byron Wallace
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MSˆ2: Multi-Document Summarization of Medical Studies (2021.emnlp-main)
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| Challenge: | Existing datasets for multi-document summarization (MDS) are either in the general domain, such as WikiSum, or very small such as DUC 1 or TAC 2011 . Existing systems for summarizing biomedical literature take 1-2 years to complete . |
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| Challenge: | Health literacy is the ability to obtain, process, and understand basic health information. |
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imapScore: Medical Fact Evaluation Made Easy (2024.findings-acl)
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| Challenge: | Using fine-grained readability measures is the first step towards making medical texts more accessible. |
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From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes (2025.emnlp-industry)
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| Challenge: | Existing automated metrics fail to align with real-world physician preferences. |
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