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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 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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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
(2004) · Cited by 9,718
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019) · arXiv · doi:10.48550/arxiv.1910.13461 · Cited by 8,816
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Wang, X., & Zhao, T. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.180 · Cited by 5
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
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
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 10
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
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
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 8
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
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
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
Motghare, M., Agarwal, M., & Agrawal, A. (2025) · Computers · doi:10.3390/computers14120508 · Cited by 5
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
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
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
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
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 6
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
Unsupervised Token-level Hallucination Detection from Summary Generation By-products
Marfurt, A., & Henderson, J. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.21 · Cited by 5
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
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
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 3
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 3
Extractive text summarisation using Bayesian state estimation of sentences: A Markovian framework
Haez, S. G., & Shamsfakhr, F. (2022) · Journal of Information Science · doi:10.1177/01655515221112842 · Cited by 2
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
Improving Next Tokens via Second-to-Last Predictions with Generate and Refine
Schneider, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91398-3_17 · Cited by 1
Balancing factual consistency and informativeness for abstractive summarization
Li, Z., Ou, L., & Huang, Y. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02724-8 · Cited by 1
Metadata
- 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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