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Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
Open accessAbstract
Neural abstractive summarization models are able to generate summaries which have high overlap with human references.However, existing models are not optimized for factual correctness, a critical metric in real-world applications.In this work, we develop a general framework where we evaluate the factual correctness of a generated summary by factchecking it automatically against its reference using an information extraction module.We further propose a training strategy which optimizes a neural summarization model with a factual correctness reward via reinforcement learning.We apply the proposed method to the summarization of radiology reports, where factual correctness is a key requirement.On two separate datasets collected from hospitals, we show via both automatic and human evaluation that the proposed approach substantially improves the factual correctness and overall quality of outputs over a competitive neural summarization system, producing radiology summaries that approach the quality of humanauthored ones.Background: radiographic examination of the chest.clinical history: 80 years of age, male ... Findings: frontal radiograph of the chest demonstrates repositioning of the right atrial lead possibly into the ivc.... a right apical pneumothorax can be seen from the image.moderate right and small left pleural effusions continue.no pulmonary edema is observed.heart size is upper limits of normal. Human Summary: pneumothorax is seen. bilateral pleural effusions continue.Summary A (ROUGE-L = 0.77): no pneumothorax is observed.bilateral pleural effusions continue.Summary B (ROUGE-L = 0.44): pneumothorax is observed on radiograph.bilateral pleural effusions continue to be seen.
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“generating Impression is related to Radiology Report Summarization (Zhang et al., 2020) where a system has to summarize the Findings section of a report into Impression , making this choice even more relevant.”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Zhang et al., 2020
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Metadata
- Authors
- Zhang, Y., Merck, D., Tsai, E., Manning, C. D., & Langlotz, C.
- Affiliations
- Stanford University (US)
- University of Florida (US)
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 5108-5120
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3034863243
- Cited by
- 171
- References
- 42
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 20 · 2023: 30 · 2022: 32 · 2021: 49 · 2020: 15
- FWCI
- 11.79
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- factual accuracy, abstractive summarization, radiology reports, fact-checking, information extraction, reinforcement learning, medical report summarization, ROUGE evaluation
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