Factual Error Correction for Abstractive Summarization Models
Open accessAbstract
Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods.However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge.We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries.The neural corrector model is pre-trained on artificial examples that are created by applying a series of heuristic transformations on reference summaries.These transformations are inspired by an error analysis of state-of-the-art summarization model outputs.Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset.We also find that transferring from artificial error correction to downstream settings is still very challenging 1 .Article: Jerusalem (CNN)The flame of remembrance burns in Jerusalem, and a song of memory haunts Valerie Braham as it never has before.(...) "Now I truly understand everyone who has lost a loved one," Braham said.Her husband, Philippe Braham, was one of 17 people killed in January's terror attacks in Paris.He was in a kosher supermarket when a gunman stormed in, killing four people, all of them Jewish.(...) Original: Valerie braham was one of 17 people killed in january's terror attacks in paris.(inconsistent) Corrected: Philippe braham was one of 17 people killed in january's terror attacks in paris.(consistent) Article: (...) Thursday's attack by al-Shabaab militants killed 147 people, including 142 students, three security officers and two university security personnel.The attack left 104 people injured, including 19 who are in critical condition, Nkaissery said.(...) Original: 147 people, including 142 students, are in critical condition.(inconsistent) Corrected: 19 people, including 142 students, are in critical condition.(inconsistent) Article: (CNN) Officer Michael Slager's five-year career with the North Charleston Police Department in South Carolina ended after he resorted to deadly force following a routine traffic stop.(...) His back is to Slager, who, from a few yards away, raises his gun and fires.Slager is now charged with murder.The FBI is involved in the investigation of the slaying of the father of four.(...) Original: Slager is now charged with murder.(consistent) Corrected: Michael Slager is now charged with murder.(consistent) Article: (CNN)The announcement this year of a new, original Dr. Seuss book sent a wave of nostalgic giddiness across Twitter, and months before publication, the number of pre-orders for "What Pet Should I Get?" continues to climb.(...) It features the spirited siblings from the beloved classic "One Fish Two Fish Red Fish Blue Fish" and is believed to have been written between 1958 and 1962.(...) Original: Seuss book sent a wave of nostalgic giddiness across twitter.(consistent) Corrected: "One Fish Two Fish Red Fish Blue Fish" book sent a wave of nostalgic giddiness across twitter.(inconsistent)
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LLMs struggle to generate accurate and up-to-date responses on current topics due to outdated knowledge, lack of domain-specific expertise, or hallucination.
“LLMs struggle to generate accurate and up-to-date responses on current topics, due to outdated knowledge, lack of domain-specific expertise, or hallucination13–17.”
From Leveraging long context in retrieval augmented language models for medical question answering (2025) · cites this paper as [13]
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Adams, G., Nguyen, B. H., Smith, J. A., Xia, Y., Xie, S., Ostropolets, A., Deb, B., Chen, Y.-J., Naumann, T., & Elhadad, N. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.587 · Cited by 9 · 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
Cl2sum: abstractive summarization via contrastive prompt constructed by LLMs hallucination
Huang, X., Nong, Q., Wang, X., Zhang, H., Du, K., Yin, C., Li, Y., Yan, B., & Zhang, X. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01795-y · Cited by 7 · see citing works
Leveraging speaker-aware structure and factual knowledge for faithful dialogue summarization
Zhao, L., Xu, W., Zhang, C., & Guo, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108550 · Cited by 7 · see citing works
Ando, K., Okumura, T., Komachi, M., Horiguchi, H., & Matsumoto, Y. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000099 · Cited by 7 · 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
Responsible AI Considerations in Text Summarization Research: A Review of Current Practices
Liu, Y. H., Cao, M., Blodgett, S. L., Cheung, J. C. K., Olteanu, A., & Trischler, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.413 · Cited by 6 · see citing works
Self-citationCritic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation
Lango, M., & Dušek, O. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.172 · Cited by 10 · see citing works
Quality Evaluation of Summarization Models for Patent Documents
Ding, J., Chen, H., Kolapudi, S. T., Pobbathi, L., & Nguyen, H. (2023) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs60937.2023.00033 · Cited by 6 · see citing works
Inspecting the Factuality of Hallucinated Entities in Abstractive Summarization.Preprint
Cao, M., Dong, Y., & Cheung, J. C. K. (2021) · arXiv (Cornell University) · Cited by 6 · see citing works
Self-citationProceedings of The 4th Workshop on e-Commerce and NLP
(2021) · doi:10.18653/v1/2021.ecnlp-1 · Cited by 6 · see citing works
Factuality Guided Diffusion-Based Abstractive Summarization
Shin, J., Park, H., & Song, H.-J. (2024) · IEEE Access · doi:10.1109/access.2024.3467063 · Cited by 5 · see citing works
Cue RAG: Dynamic multi-output cue memory under H framework for retrieval-augmented generation
Fu, Y., Liu, D., Zhang, B., Jiang, Z., Mei, H., & Guan, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130235 · Cited by 5 · see citing works
Improving Factual Consistency of Abstractive Summarization on Customer Feedback
Liu, Y., Sun, Y., & Gao, V. (2021) · Workshop on e-Commerce and NLP (ECNLP) · doi:10.18653/v1/2021.ecnlp-1.19 · Cited by 7 · see citing works
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization
Zeng, Z., Chen, J., Xu, W., & Li, L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.337 · Cited by 5 · see citing works
Wang, X., & Zhao, T. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.180 · Cited by 5 · see citing works
Rong, H., Chen, G., Ma, T., Sheng, V. S., & Bertino, E. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3286097 · Cited by 5 · see citing works
Improving Faithfulness by Augmenting Negative Summaries from Fake Documents
Wang, T., Ladhak, F., Durmus, E., & He, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.816 · Cited by 5 · see citing works
Sequentially Controlled Text Generation
Spangher, A., Ming, Y., Hua, X., & Peng, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.509 · Cited by 7 · see citing works
Mitigating Intrinsic Named Entity-Related Hallucinations of Abstractive Text Summarization
Shen, J., Xuan, J., & Liang, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1059 · Cited by 5 · see citing works
Towards improving coherence and diversity of slogan generation
Jin, Y., Bhatia, A., Wanvarie, D., & Le, P. T. V. (2022) · Natural Language Engineering · doi:10.1017/s1351324921000474 · Cited by 6 · see citing works
Shortcomings of Question Answering Based Factuality Frameworks for Error Localization
Kamoi, R., Goyal, T., & Durrett, G. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.11 · Cited by 5 · see citing works
Multi-Fact Correction in Abstractive Text SummarizationPreprint
Dong, Y., Wang, S., Gan, Z., Cheng, Y., Cheung, J. C. K., & Liu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02443 · Cited by 5 · see citing works
Self-citationMoFE: Mixture of Factual Experts for Controlling Hallucinations in Abstractive Summarization.Preprint
Choubey, P. K., Vig, J., Liu, W., & Rajani, N. F. (2021) · arXiv (Cornell University) · Cited by 5 · see citing works
Li, Y., Li, L., Hu, D., Li, Y., Zhou, Y., Litvak, M., & Vanetik, N. (2023) · Research Square · doi:10.21203/rs.3.rs-3695932/v1 · Cited by 7 · see citing works
Motghare, M., Agarwal, M., & Agrawal, A. (2025) · Computers · doi:10.3390/computers14120508 · Cited by 5 · see citing works
A Survey of Factual Consistency in Summarization from 2021 to 2023
Li, Y., Hao, X., & Li, L. (2023) · International Conference on Computers and Artificial Intelligence Technology (CAIT) · doi:10.1109/cait59945.2023.10468958 · Cited by 4 · see citing works
Gao, M., Wan, X., Su, J., Wang, Z., & Huai, B. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.779 · Cited by 5 · see citing works
Improving named entity correctness of abstractive summarization by generative negative sampling
Chen, Z., & Lin, H. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101504 · Cited by 4 · see citing works
Lee, D., Lim, J., Whang, T., Lee, C., Cho, S., Park, M., & Lim, H. (2021) · Workshop on New Frontiers in Summarization (NewSum) · doi:10.18653/v1/2021.newsum-1.8 · Cited by 4 · see citing works
Nandwani, Y., Kumar, V., Raghu, D., Joshi, S., & Lastras, L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.639 · Cited by 5 · see citing works
Zhu, Y., Li, Y., Li, L., Hu, D., Hao, X., Chen, D., Zhang, X., Zhang, Z., Zhou, Y., Litvak, M., et al. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3567744 · Cited by 4 · see citing works
Unsupervised Token-level Hallucination Detection from Summary Generation By-products
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Chen, W., Wu, C.-K., Chen, H., & Chen, C.-C. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.54 · Cited by 4 · see citing works
Lan, Z., Li, W., Su, J., Xiao, X., Liu, J., Wu, W., & Lyu, Y. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14267 · Cited by 3 · see citing works
He, X., Jin, A.-L., Ma, J., Yuan, Y., & Yiu, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.667 · Cited by 2 · see citing works
ESTIME: Estimation of Summary-to-Text Inconsistency by Mismatched Embeddings
Vasilyev, O., & Bohannon, J. (2021) · Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) · doi:10.18653/v1/2021.eval4nlp-1.10 · Cited by 2 · see citing works
Extractive text summarisation using Bayesian state estimation of sentences: A Markovian framework
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Mitigating Media Bias through Neutral Article GenerationPreprint
Lee, N., Bang, Y., Madotto, A., & Fung, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00336 · Cited by 6 · see citing works
Improving Next Tokens via Second-to-Last Predictions with Generate and Refine
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Balancing factual consistency and informativeness for abstractive summarization
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- Authors
- Cao, M., Dong, Y., Wu, J., & Cheung, J. C. K.
- Affiliations
- Mila - Quebec Artificial Intelligence Institute (CA)
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 6251-6258
- OpenAlex
- W3102645206
- Cited by
- 132
- References
- 19
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 11 · 2023: 43 · 2022: 29 · 2021: 25 · 2020: 3
- FWCI
- 9.55
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- Compute Canada
- Natural Sciences and Engineering Research Council of Canada
- Fonds de recherche du Québec – Nature et technologies
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- abstractive summarization, factual error correction, factual consistency, post-editing, CNN/DailyMail, self-supervised pre-training, error analysis
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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