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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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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Self-citation(2014) · Cited by 33,419
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Self-citationIntroduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Self-citation(2004) · Cited by 9,718
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,252
Self-citationA Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369
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Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
“[231] propose to explicitly measure the factual correctness of a generated text against the reference by first extracting facts via an information extraction (IE) module.”
BackgroundNeural Natural Language Processing for unstructured data in electronic health records: A review
Li, I., Pan, J. N., Goldwasser, J., Verma, N., Wong, W. P., Nuzumlalı, M. Y., Rosand, B., Li, Y., Zhang, M. M., Chang, D., et al. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100511 · Cited by 280
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
Laban, P., Schnabel, T., Bennett, P. N., & Hearst, M. A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00453 · Cited by 238
A systematic review of natural language processing applied to radiology reports
Casey, A., Davidson, E., Poon, M., Dong, H., Duma, D., Grivas, A., Grover, C., Suárez-Paniagua, V., Tobin, R., Whiteley, W., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01533-7 · Cited by 244
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Celikyilmaz, A., Clark, E., & Gao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.14799 · Cited by 207
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Manning, C. D. (2022) · Daedalus · doi:10.1162/daed_a_01905 · Cited by 193
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Cao, M., Dong, Y., Wu, J., & Cheung, J. C. K. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.506 · Cited by 132
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Zhu, C., Hinthorn, W., Xu, R., Zeng, Q., Zeng, M., Huang, X., & Jiang, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.58 · Cited by 127
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Williams, C. Y. K., Subramanian, C. R., Ali, S. S., Apolinario, M., Askin, E., Barish, P. N., Cheng, M., Deardorff, W. J., Donthi, N., Ganeshan, S., et al. (2025) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2025.0821 · Cited by 127
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Messina, P., Pino, P., Parra, D., Soto, Á., Besa, C., Uribe, S. A., Andía, M. E., Cristián Tejos, N., Prieto, C., & Daniel Capurro, N. (2022) · ACM Computing Surveys · doi:10.1145/3522747 · Cited by 111
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Cai, X., Liu, S., Han, J., Yang, L., Liu, Z., & Liu, T. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3132724 · Cited by 72
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Abacha, A. B., Mrabet, Y., Zhang, Y., Shivade, C., Langlotz, C. P., & Demner‐Fushman, D. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.8 · Cited by 69
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“F 1 CheXbert is the F1-score between the prediction of CheXbert over the generated report ˆ y and the corresponding reference y .”
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Searle, T., Ibrahim, Z. M., Teo, J., & Dobson, R. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104358 · Cited by 62
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Guo, Y., Qiu, W., Gondy Leroy, A., Wang, S., & Cohen, T. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104580 · Cited by 62
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Zhong, W., Tang, D., Feng, Z., Duan, N., Zhou, M., Gong, M., Shou, L., Jiang, D., Wang, J., & Yin, J. P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.539 · Cited by 53
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Hu, J., Li, Z., Chen, Z., Li, Z., Wan, X., & Chang, T. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.320 · Cited by 51
Zambrano Chaves, J. M., Huang, S.-C., Xu, Y., Xu, H., Usuyama, N., Zhang, S., Wang, F., Xie, Y., Khademi, M., Yang, Z., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58344-x · Cited by 45
“In particular, we investigate two key technical ideas used in LLaVA-Rad: the effect of pre-training a domain-speci fi c image encoder using 697 thousand diverse CXR image-text pairs (Fig.”
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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