Improving Factual Consistency of Abstractive Summarization via Question Answering
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Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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Nan et al. propose an entity-based metric relying on the Named-Entity Recognition model, which is relatively more robust.
“Therefore, Nan et al. [190] propose an entity-based metric relying on the Named-Entity Recognition model, which is relatively more robust.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as Nan et al. [190]
Section 5 (Related work; page not available) says Nan et al. proposed entity-level factual-consistency metrics. It does not say those metrics rely on a Named-Entity Recognition model or are relatively more robust, so those parts of the claim are unsupported by our copy. (LLM, conf=0.82)
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Dutta, T., & Liu, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650810 · Cited by 1 · see citing works
Consecutive Question Generation via Dynamic Multitask Learning
Li, Y., Li, S., & Shi, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.493 · Cited by 1 · see citing works
Toward Fact-aware Abstractive Summarization Method Using Joint LearningPreprint
Mao, X., Chen, L., Ju, S., Wan, X., & Liu, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-2206382/v1
Factual Error Correction in Summarization with Retriever-Reader Pipeline
Li, W., Liu, J., & Gao, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-30105-6_33
Multi-doc Hybrid Summarization via Salient Representation Learning
Xiao, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.37
Leveraging In-and-Cross Project Pseudo-Summaries for Project-Specific Code Summarization
Wu, Y., Hu, T., Liao, N., Xie, R., Zhang, M., Du, D., & Lin, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650905
KQFV: a knowledge-enhanced method using question answering for fact verification
Bian, Y., & Ma, T. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00956-2
Chen, D., Hu, D., & Li, L. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_33
Hallucinations in Abstractive Text Summarization with Large Language Models
Delpisheh, N., & Chali, Y. (2025) · International Generative AI and Computational Language Modelling Conference (GACLM) · doi:10.1109/gaclm67198.2025.11231924 · Cited by 1 · see citing works
Knowledge Extraction, Fusion and Summary Rewriting: A Three-Stage Modular Abstractive Summarization
Wu, S. J., & Liu, J. (2025) · IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) · doi:10.1109/icsece65727.2025.11256782
Localizing Factual Inconsistencies in Attributable Text Generation
Cattan, A., Roit, P., Zhang, S., Wan, D., Aharoni, R., Szpektor, I., Bansal, M., & Dagan, I. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.598 · Cited by 3 · see citing works
Sebire, I. G., Barale, C., & Lapata, M. (2025) · International Conference on Artificial Intelligence and Law · doi:10.1145/3769126.3769237
Pratama, I. P. A. E., Widyantara, I. M. O., Linawati, L., & Gunantara, N. (2026) · International Journal of Advances in Data and Information Systems · doi:10.59395/ijadis.v7i1.1488
Integrating Unsupervised Learning for the Factual Consistency of Generative Models
Nair, S., & -, Y. S. R. (2026) · Future Internet · doi:10.3390/fi18050235
NEWSLYTIC: An AI-Powered News Analysis Engine
Thiyagu, T. M., Mohana Priya, M., & Lakshmi, M. (2026) · IEEE International Conference on Emerging Computing and Intelligent Technologies (ICoECIT) · doi:10.1109/icoecit68303.2026.11496850
Temporal and linguistic enrichment for abstractive text summarization using transformer models
Abdalgader, K., Ahmed, F. Y. H., Wassie, B., Kerala, K., Heramo, N., & Gudeta, R. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01710-5
AI-Powered Like-Minded Team Builder
Lakshmi, R. T. T., Yashwanth, P., & Vignesh, N. (2026) · IEEE Global Symposium on Emerging and Communication Technologies (GSEACT) · doi:10.1109/gseact68539.2026.11619987
Zhu, Y., Zhang, X., Gao, J., Li, L., & Wang, X. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01349-3
- Authors
- Nan, F., Nogueira dos Santos, C., Zhu, H., Ng, P., McKeown, K., Nallapati, R., Zhang, D., Wang, Z., Arnold, A. O., & Xiang, B.
- Affiliations
- Columbia University (US)
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2021
- Pages
- 6881-6894
- OpenAlex
- W3177242492
- Cited by
- 78
- References
- 29
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 8 · 2023: 33 · 2022: 17 · 2021: 1
- FWCI
- 6.82
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- 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 consistency, question answering, factual accuracy, summarization 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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