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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Generating the Impression section is related to Radiology Report Summarization, in which a system summarizes the Findings section of a report into the Impression section.
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
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 · see citing works
Self-citation(2014) · Cited by 33,419 · see citing works
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 · see citing works
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278 · see citing works
Self-citationIntroduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288 · see citing works
Self-citation(2004) · Cited by 9,718 · see citing works
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works
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 7,171 · see citing works
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 · see citing works
Get To The Point: Summarization with Pointer-Generator Networks
See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775 · see citing works
Self-citationLexRank: Graph-based Lexical Centrality as Salience in Text Summarization
Erkan, G., & Radev, D. R. (2004) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1523 · Cited by 2,975 · see citing works
BERTScore: Evaluating Text Generation with BERT
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865 · see citing works
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Nallapati, R., Zhou, B., dos Santos, C., Gulcehre, C., & Xiang, B. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1028 · Cited by 2,361 · see citing works
Self-Critical Sequence Training for Image Captioning
Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.131 · Cited by 2,177 · see citing works
Vinyals, O., Fortunato, M., & Jaitly, N. (2015) · neural information processing systems · Cited by 2,024 · see citing works
Nallapati, R. M., Zhai, F., & Zhou, B. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10958 · Cited by 1,262 · see citing works
A Deep Reinforced Model for Abstractive Summarization
Paulus, R., Xiong, C., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1705.04304 · Cited by 1,182 · see citing works
Evaluating the Factual Consistency of Abstractive Text Summarization
Kryscinski, W., McCann, B., Xiong, C., & Socher, R. (2019) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.1910.12840 · Cited by 1,093
Neural Summarization by Extracting Sentences and Words
Cheng, J., & Lapata, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1046 · Cited by 802 · see citing works
Evaluating the Factual Consistency of Abstractive Text Summarization
Kryscinski, W., McCann, B., Xiong, C., & Socher, R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.750 · Cited by 669 · see citing works
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
Chen, Y.-C., & Bansal, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1063 · Cited by 601 · see citing works
Generating Wikipedia by Summarizing Long SequencesPreprint
Liu, P. J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, Ł., & Shazeer, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.10198 · Cited by 578 · see citing works
Sequence Level Training with Recurrent Neural Networks
Ranzato, M., Chopra, S., Auli, M., & Zaremba, W. (2016) · International Conference on Learning Representations · Cited by 572 · see citing works
Challenges in Data-to-Document Generation
Wiseman, S., Shieber, S., & Rush, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1239 · Cited by 524 · see citing works
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · see citing works
Self-citationFaithful to the Original: Fact Aware Neural Abstractive Summarization
Cao, Z., Wei, F., Li, W., & Li, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11912 · Cited by 359 · see citing works
Neural Text Summarization: A Critical Evaluation
Kryscinski, W., Keskar, N. S., McCann, B., Xiong, C., & Socher, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1051 · Cited by 335 · see citing works
Breaking the Softmax Bottleneck: A High-Rank RNN Language ModelPreprint
Yang, Z., Dai, Z., Salakhutdinov, R., & Cohen, W. W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.03953 · Cited by 291 · see citing works
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
Li, C. Y., Liang, X., Hu, Z., & Xing, E. P. (2018) · arXiv · doi:10.48550/arxiv.1805.08298 · Cited by 271 · see citing works
Falke, T., Ribeiro, L. F. R., Utama, P. A., Dagan, I., & Gurevych, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1213 · Cited by 265 · see citing works
Toward Best Practices in Radiology Reporting
Kahn, C. E., Langlotz, C. P., Burnside, E. S., Carrino, J. A., Channin, D. S., Hovsepian, D. M., & Rubin, D. L. (2009) · Radiology · doi:10.1148/radiol.2523081992 · Cited by 245 · see citing works
Self-citationClinically Accurate Chest X-Ray Report GenerationPreprint
Liu, G., Hsu, T.-M. H., McDermott, M. B. A., Boag, W., Weng, W., Peter Szolovits, B., & Ghassemi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02633 · Cited by 212 · see citing works
BanditSum: Extractive Summarization as a Contextual Bandit
Dong, Y., Shen, Y., Crawford, E., van Hoof, H., & Cheung, J. C. K. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1409 · Cited by 171 · see citing works
Assessing The Factual Accuracy of Generated Text
Goodrich, B., Rao, V., Liu, P. J., & Saleh, M. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330955 · Cited by 162 · see citing works
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Dhingra, B., Faruqui, M., Parikh, A., Chang, M.-W., Das, D., & Cohen, W. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1483 · Cited by 150 · see citing works
Learning to Summarize Radiology Findings
Zhang, Y., Ding, D. Y., Qian, T., Manning, C. D., & Langlotz, C. P. (2018) · Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis · doi:10.18653/v1/w18-5623 · Cited by 126 · see citing works
Self-citationThe price of debiasing automatic metrics in natural language evalaution
Chaganty, A., Mussmann, S., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1060 · Cited by 96 · see citing works
Ontology-Aware Clinical Abstractive Summarization
MacAvaney, S., Sotudeh, S., Cohan, A., Goharian, N., Talati, I. A., & Filice, R. W. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331319 · Cited by 74 · see citing works
Sequence Level Training with Recurrent Neural Networks
Ranzato, M., Chopra, S., Auli, M., & Zaremba, W. (2015) · arXiv · doi:10.48550/arxiv.1511.06732
Flagged first, then by citations
2 of the 42 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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 · see citing works
“[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 262 · see citing works
<scp>SummaC</scp>: 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 · see citing works
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 234 · see citing works
Evaluation of Text Generation: A Survey
Celikyilmaz, A., Clark, E., & Gao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.14799 · Cited by 207 · see citing works
Contrastive Attention for Automatic Chest X-ray Report Generation
Liu, F., Yin, C., Wu, X., Ge, S., Zhang, P., & Sun, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.23 · Cited by 175 · see citing works
Human Language Understanding & Reasoning
Manning, C. D. (2022) · Daedalus · doi:10.1162/daed_a_01905 · Cited by 173 · see citing works
Self-citationImproving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
Miura, Y., Zhang, Y., Tsai, E., Langlotz, C., & Jurafsky, D. (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.416 · Cited by 149 · see citing works
Self-citationContrastive Triple Extraction with Generative Transformer
Ye, H., Zhang, N., Deng, S., Chen, M., Tan, C., Huang, F., & Chen, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17677 · Cited by 146 · see citing works
Competence-based Multimodal Curriculum Learning for Medical Report Generation
Liu, F., Ge, S., & Wu, X. (2021) · doi:10.18653/v1/2021.acl-long.234 · Cited by 145 · see citing works
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
Cao, S., & Wang, L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.532 · Cited by 135 · see citing works
Factual Error Correction for Abstractive Summarization Models
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 · see citing works
Enhancing Factual Consistency of Abstractive Summarization
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 · see citing works
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
Krishna, K., Khosla, S., Bigham, J. P., & Lipton, Z. C. (2021) · doi:10.18653/v1/2021.acl-long.384 · Cited by 123 · see citing works
Physician- and Large Language Model–Generated Hospital Discharge Summaries
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 123 · see citing works
Hurdles to Progress in Long-form Question Answering
Krishna, K., Roy, A., & Iyyer, 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.393 · Cited by 114 · see citing works
A vision–language foundation model for the generation of realistic chest X-ray images
Bluethgen, C., Chambon, P., Delbrouck, J.-B., van der Sluijs, R., Połacin, M., Zambrano Chaves, J. M., Abraham, T. M., Purohit, S., Langlotz, C. P., & Chaudhari, A. S. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01246-y · Cited by 108 · see citing works
Self-citationA Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images
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 103 · see citing works
Annotating and Modeling Fine-grained Factuality in Summarization
Goyal, T., & Durrett, G. (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.114 · Cited by 100 · see citing works
Reducing Quantity Hallucinations in Abstractive Summarization
Zhao, Z., Cohen, S. B., & Webber, B. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.203 · Cited by 99 · see citing works
Automated Radiology Report Generation: A Review of Recent Advances
Sloan, P., Clatworthy, P. L., Simpson, E., & Mirmehdi, M. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3408456 · Cited by 98 · see citing works
Abstractive text summarization: State of the art, challenges, and improvements
Shakil, H., Farooq, A., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128255 · Cited by 89 · see citing works
Developing and evaluating large language model–generated emergency medicine handoff notes
Hartman, V., Zhang, X., Poddar, R., McCarty, M., Fortenko, A., Sholle, E., Sharma, R., Campion, T., & Steel, P. A. D. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.48723 · Cited by 76 · see citing works
ChestXRayBERT: A Pretrained Language Model for Chest Radiology Report Summarization
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 · see citing works
Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain
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 67 · see citing works
Self-citationA Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning
Zhang, M., Zhou, G., Yu, W., Huang, N., & Liu, W. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7132226 · Cited by 67 · see citing works
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards
Delbrouck, J.-B., Chambon, P., Bluethgen, C., Tsai, E., Almusa, O., & Langlotz, C. P. (2022) · arXiv · doi:10.48550/arxiv.2210.12186 · Cited by 63 · see citing works
“F 1 CheXbert is the F1-score between the prediction of CheXbert over the generated report ˆ y and the corresponding reference y .”
BackgroundMethodInfluentialSelf-citationA Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., & Dolan, B. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.464 · Cited by 63 · see citing works
Self-citationThe Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Huang, Y., Feng, X., Feng, X., & Qin, B. (2021) · arXiv · doi:10.48550/arxiv.2104.14839 · Cited by 62 · see citing works
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors
Tang, L., Goyal, T., Fabbri, A. R., Laban, P., Xu, J., Yavuz, S., Kryściński, W., Rousseau, J. F., & Durrett, G. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.650 · Cited by 62 · see citing works
Faithful AI in Medicine: A Systematic Review with Large Language Models and BeyondPreprint
Xie, Q., Schenck, E. J., Yang, H. S., Chen, Y., Peng, Y., & Wang, F. (2023) · medRxiv · doi:10.1101/2023.04.18.23288752 · Cited by 57 · see citing works
The Summary Loop: Learning to Write Abstractive Summaries Without Examples
Laban, P., Hsi, A., Canny, J., & Hearst, M. A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.460 · Cited by 53 · see citing works
Controllable Neural Dialogue Summarization with Personal Named Entity Planning
Liu, Z., & Chen, N. F. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.8 · Cited by 52 · see citing works
Searle, T., Ibrahim, Z. M., Teo, J., & Dobson, R. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104358 · Cited by 52 · see citing works
Retrieval augmentation of large language models for lay language generation
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 52 · see citing works
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
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 50 · see citing works
Graph Enhanced Contrastive Learning for Radiology Findings Summarization
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 48 · see citing works
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 · see citing works
“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.”
Self-citationWhat’s in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
Adams, G., Alsentzer, E., Ketenci, M., Zucker, J. E., & Elhadad, N. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.382 · Cited by 45 · see citing works
Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation
Xie, Y., Sun, F., Deng, Y., Li, Y., & Ding, B. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.10 · Cited by 44 · see citing works
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation
Moramarco, F., Papadopoulos Korfiatis, A., Perera, M., Juric, D., Flann, J., Reiter, E., Belz, A., & Savkov, A. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.394 · Cited by 40 · see citing works
Contrastive Information Extraction With Generative Transformer
Zhang, N., Ye, H., Deng, S., Tan, C., Chen, M., Huang, S., Huang, F., & Chen, H. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3110126 · Cited by 40 · see citing works
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations
Zhang, L., Negrinho, R. M. P., Ghosh, A., Jagannathan, V., Hassanzadeh, H. R., Schaaf, T., & Gormley, M. R. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.313 · Cited by 38 · see citing works
Diagnostic captioning: a survey
Pavlopoulos, J., Kougia, V., Androutsopoulos, I., & Papamichail, D. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01684-7 · Cited by 37 · see citing works
Multilingual Summarization with Factual Consistency Evaluation
Aharoni, R., Narayan, S., Maynez, J., Herzig, J., Clark, E., & Lapata, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.220 · Cited by 36 · see citing works
Contrastive Attention for Automatic Chest X-ray Report GenerationPreprint
Liu, F., Yin, C., Wu, X., Ge, S., Zou, Y., Zhang, P., Zou, Y., & Sun, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06965 · Cited by 36 · see citing works
FAR-ASS: Fact-aware reinforced abstractive sentence summarization
Zhang, M., Zhou, G., Yu, W., & Liu, W. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2020.102478 · Cited by 35 · see citing works
Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation
Sun, L., Zhao, J. J., Han, W., & Xiong, C. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.28 · Cited by 34 · see citing works
Leveraging Summary Guidance on Medical Report Summarization
Zhu, Y., Yang, X., Wu, Y., & Zhang, W. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3304376 · Cited by 33 · see citing works
Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text
Laban, P., Schnabel, T., Bennett, P. N., & Hearst, M. A. (2021) · doi:10.18653/v1/2021.acl-long.498 · Cited by 32 · see citing works
Learning to Revise References for Faithful Summarization
Adams, G., Shing, H.-C., Sun, Q., Winestock, C., McKeown, K. R., & Elhadad, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.296 · Cited by 30 · see citing works
King, D., Shen, Z., Subramani, N., Weld, D. S., Beltagy, I., & Downey, D. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.51 · Cited by 28 · see citing works
ViLMedic: a framework for research at the intersection of vision and language in medical AI
Delbrouck, J.-B., Saab, K. K., Varma, M., Eyuboglu, S., Chambon, P., Dunnmon, J., Chaves, J. M. Z., Chaudhari, A. S., & Langlotz, C. P. (2022) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2022.acl-demo.3 · Cited by 28 · see citing works
Self-citationNishio, M., Matsunaga, T., Matsuo, H., Nogami, M., Kurata, Y., Fujimoto, K., Sugiyama, O., Akashi, T., Aoki, S., & Murakami, T. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101465 · Cited by 27 · see citing works
Question-aware transformer models for consumer health question summarization
Yadav, S., Gupta, D., Abacha, A. B., & Demner‐Fushman, D. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104040 · Cited by 26 · see citing works
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
Van Veen, D., Van Uden, C., Attias, M., Pareek, A., Bluethgen, C., Polacin, M., Chiu, W., Delbrouck, J.-B., Zambrano Chaves, J., Langlotz, C., et al. (2023) · The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks · doi:10.18653/v1/2023.bionlp-1.42 · Cited by 25 · see citing works
Self-citationPatient Information Summarization in Clinical Settings: Scoping Review
Keszthelyi, D., Gaudet-Blavignac, C., Bjelogrlic, M., & Lovis, C. (2023) · JMIR Medical Informatics · doi:10.2196/44639 · Cited by 24 · see citing works
Úbeda, P. L., Martín‐Noguerol, T., Díaz-Angulo, C., & Luna, A. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105443 · Cited by 24 · see citing works
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation
Nishino, T., Ozaki, R., Momoki, Y., Taniguchi, T., Kano, R., Nakano, N., Tagawa, Y., Taniguchi, M., Ohkuma, T., & Nakamura, K. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.202 · Cited by 24 · see citing works
Optimizing radiologist productivity and efficiency: Work smarter, not harder
McGrath, A. L., Dodelzon, K., Awan, O. A. R., Said, N., & Bhargava, P. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.110131 · Cited by 24 · see citing works
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior NotesPreprint
Shing, H.-C., Shivade, C., Pourdamghani, N., Nan, F., Resnik, P., Oard, D. W., & Bhatia, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13498 · Cited by 24 · see citing works
PriMock57: A Dataset Of Primary Care Mock Consultations
Papadopoulos Korfiatis, A., Moramarco, F., Sarac, R., & Savkov, A. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2022.acl-short.65 · Cited by 23 · see citing works
Customized Impression Prediction From Radiology Reports Using BERT and LSTMs
Gündoğdu, B., Pamuksuz, U., Chung, J. H., Telleria, J. M., Liu, P., Khan, F., & Chang, P. J. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3086435 · Cited by 22 · see citing works
BDKG at MEDIQA 2021: System Report for the Radiology Report Summarization Task
Dai, S., Wang, Q., Lyu, Y., & Zhu, Y. T. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.11 · Cited by 21 · see citing works
Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges
Ahsan, H., McInerney, D. J., Kim, J., Potter, C., Young, G. S., Amir, S., & Wallace, B. (2023) · PubMed · doi:10.48550/arxiv.2309.04550 · Cited by 21 · see citing works
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View
Liu, T., Zheng, X., Chang, B., & Sui, Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17583 · Cited by 21 · see citing works
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards
Yadav, S., Gupta, D. K., Abacha, A. B., & Demner‐Fushman, D. (2021) · doi:10.18653/v1/2021.acl-short.33 · Cited by 20 · see citing works
Controllable Summarization with Constrained Markov Decision Process
Chan, H. P., Wang, L., & King, I. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00423 · Cited by 20 · see citing works
Automatic Medical Report Generation: Methods and Applications
Li, G., Tahir, A. M., Zhang, D., Wang, Z. J., & Ward, R. K. (2024) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.20240044 · Cited by 19 · see citing works
TODSum: Task-Oriented Dialogue Summarization with State TrackingPreprint
Zhao, L., Zheng, F., He, K., Zeng, W., Lei, Y., Jiang, H., Wu, W., Xu, W., Guo, J. Q., & Meng, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.12680 · Cited by 19 · see citing works
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization
Karn, S. K., Liu, N., Schuetze, H., & Farri, O. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.109 · Cited by 18 · see citing works
Fine-Tuning BART for Abstractive Reviews Summarization
Yadav, H. N., Patel, N., & Jani, D. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-7346-8_32 · Cited by 18 · see citing works
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
Wang, L. L., Otmakhova, Y., DeYoung, J., Truong, T. H., Kuehl, B., Bransom, E., & Wallace, B. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.549 · Cited by 18 · see citing works
Discourse Understanding and Factual Consistency in Abstractive Summarization
Gabriel, S., Bosselut, A., Da, J., Holtzman, A., Buys, J., Lo, K., Çelikyılmaz, A., & Choi, Y. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.34 · Cited by 17 · see citing works
QIAI at MEDIQA 2021: Multimodal Radiology Report Summarization
Delbrouck, J.-B., Zhang, C., & Rubin, D. L. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.33 · Cited by 17 · see citing works
SEASum: Syntax-Enriched Abstractive Summarization
Liu, S., Yang, L., & Cai, X. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116819 · Cited by 17 · see citing works
Dehkordi, M. K. H., Perl, Y., Deek, F. P., He, Z., Keloth, V. K., Liu, H., Elhanan, G., & Einstein, A. J. (2025) · JMIR Medical Informatics · doi:10.2196/66476 · Cited by 16 · see citing works
Delbrouck, J.-B., Varma, M., Chambon, P., & Langlotz, C. P. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.45 · Cited by 16 · see citing works
Self-citationA Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization
Adams, G., Zucker, J. E., & Elhadad, N. (2023) · PubMed · doi:10.48550/arxiv.2303.03948 · Cited by 15 · see citing works
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
Zhang, N., Zhang, Y., Guo, W., Mitra, P., & Zhang, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.673 · Cited by 15 · see citing works
Self-citationParres, D., Albiol, A., & Paredes, R. (2024) · Bioengineering · doi:10.3390/bioengineering11040351 · Cited by 14 · see citing works
A New Approach to Overgenerating and Scoring Abstractive Summaries
Song, K., Wang, B., Feng, Z., & Liu, F. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.110 · Cited by 14 · see citing works
Alambo, A., Banerjee, T., Thirunarayan, K., & Cajita, M. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871798 · Cited by 14 · see citing works
Cross-modal knowledge guided model for abstractive summarization
Wang, H., Liu, J., Duan, M., Gong, P., Wu, Z., Wang, J., & Han, B. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01170-9 · Cited by 14 · see citing works
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive SummarizationPreprint
Cao, S., & Wang, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.09209 · Cited by 14 · see citing works
Inspecting state of the art performance and NLP metrics in image-based medical report generation
Pino, P., Parra, D., Besa, C., & Uribe, S. A. (2020) · LatinX in AI at Neural Information Processing Systems Conference (LXAI) · doi:10.52591/lxai202012128 · Cited by 13 · see citing works
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization
Dreyer, M., Liu, M., Nan, F., Atluri, S., & Ravi, S. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.156 · Cited by 12 · see citing works
RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification
Jaiswal, A. K., Tang, L., Ghosh, M., Rousseau, J. F., Peng, Y., & Ding, Y. (2021) · PubMed · doi:10.48550/arxiv.2110.15426 · Cited by 12 · see citing works
UGDAS: Unsupervised graph-network based denoiser for abstractive summarization in biomedical domain
Du, Y., Zhao, Y., Yan, J., & Li, Q. (2022) · Methods · doi:10.1016/j.ymeth.2022.03.012 · Cited by 12 · see citing works
Abstractive summarization with deep reinforcement learning using semantic similarity rewards
Fikri, F. B., Oflazer, K., & Yanikoglu, B. A. (2023) · Natural Language Engineering · doi:10.1017/s1351324923000505 · Cited by 12 · see citing works
Toward Expanding the Scope of Radiology Report Summarization to Multiple Anatomies and Modalities
Chen, Z., Varma, M., Wan, X., Langlotz, C. P., & Delbrouck, J.-B. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.41 · Cited by 11 · see citing works
Self-citationExtracting Summary Knowledge Graphs from Long DocumentsPreprint
Wu, Z., Koncel-Kedziorski, R., Mari Ostendorf, E., & Hajishirzi, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.09162 · Cited by 11 · see citing works
Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice
Draper, T. C., Cox, T., Lamb-Riddell, K., Moretti, L. A., McCormick, J., Trowell, S., Kiely, J., & Luxton, R. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000092 · Cited by 10 · see citing works
ChatGPT based contrastive learning for radiology report summarization
Luo, Z., Jiang, Z., Wang, M., Cai, X., Gao, D., & Yang, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125827 · Cited by 10 · see citing works
Impact of acoustic and informational noise on AI-generated clinical summaries
Draper, T. C., Leake, J., Lamb-Riddell, K., Cox, T. M., McCormick, J., Trowell, S., Kiely, J., & Luxton, R. W. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000057 · Cited by 10 · see citing works
paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization
Zhu, W., He, Y., Chai, L., Fan, Y., Ni, Y., Xie, G., & Wang, X. (2021) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2021.bionlp-1.10 · Cited by 10 · see citing works
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization
Zhao, L., Zeng, W., Xu, W., & Guo, J. H. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.209 · Cited by 10 · see citing works
SUMMIT: Scaffolding Open Source Software Issue Discussion Through Summarization
Gilmer, S., Bhat, A., Shah, S. A. A., Cherry, K., Cheng, J., & Guo, J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610088 · Cited by 10 · see citing works
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization
Huang, K.-H., Singh, S., Ma, X., Xiao, W., Nan, F., Dingwall, N., Wang, W. Y., & McKeown, K. R. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.37 · Cited by 10 · see citing works
- 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 ›
Are you an author, or do you see a problem with this page? Request a review
