The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Evaluating the Factual Consistency of Abstractive Text Summarization
Open accessAbstract
Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary.Training data is generated by applying a series of rule-based transformations to the sentences of source documents.The factual consistency model is then trained jointly for three tasks: 1) identify whether sentences remain factually consistent after transformation, 2) extract a span in the source documents to support the consistency prediction, 3) extract a span in the summary sentence that is inconsistent if one exists.Transferring this model to summaries generated by several state-of-the art models reveals that this highly scalable approach substantially outperforms previous models, including those trained with strong supervision using standard datasets for natural language inference and fact checking.Additionally, human evaluation shows that the auxiliary span extraction tasks provide useful assistance in the process of verifying factual consistency.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In most previous works, factuality has been defined as whether a claim in a text can be supported by evidence from a separate, trustworthy knowledge base, with applications in fact-checking (Thorne et al., 2018b) (where the knowledge base is a large source like Wikipedia) and summarization (Kryscinski et al., 2020) (where the knowledge base is an input document or documents).”
From FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios (2023) · cites this paper as Kryscinski et al., 2020
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Additionally, hallucinations are observed over a wide range of tasks beyond pure fact verification (Kryscinski et al., 2020; Maynez et al., 2020).”
From SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023) · cites this paper as Kryscinski et al., 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Research suggests that up to 30% of machine-generated abstractive summaries may contain factual inconsistencies [52, 53].”
From All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs? (2026) · cites this paper as [53]
The cited source says what the claim attributes to it.
In the citing paper
“Results show that the performance of their FactCC model surpasses the classifiers trained on the MNLI or FEVER datasets.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [126]
The cited source says what the claim attributes to it.
In the citing paper
“These methods, such as entity swapping and sentence negation (Kryscinski et al., 2020;Goyal and Durrett, 2021), were designed to target specific error types that occurred in claims from earlier summarization models.”
From MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (2024) · cites this paper as Kryscinski et al., 2020
The cited source says what the claim attributes to it.
In the citing paper
“In response to this challenge, a surge of research explores leveraging data-augmentation methods to construct synthetical data for fine-tuning the classifier, either by rule-based perturbation [79, 152, 266] or generation [389].”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [152]
The cited source says what the claim attributes to it.
In the citing paper
“most prior studies have primarily concentrated on task-specific hallucinations, such as abstractive summarization [81, 102, 152, 208, 236, 310], data-to-text[240, 296], and machine translation [389].”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [152]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“This approach has been widely studied in the summarization literature and recent work in factual precision (Kryscinski et al., 2020; Wang et al., 2020; Gao et al., 2022; Manakul et al., 2023).”
From FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023) · cites this paper as Kryscinski et al., 2020
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
(2017) · Cited by 11,625
(2004) · Cited by 9,718
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
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Williams, A., Nangia, N., & Bowman, S. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1101 · Cited by 3,342
A Neural Attention Model for Abstractive Sentence Summarization
Rush, A. M., Chopra, S., & Weston, J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1044 · Cited by 2,932
Research works printed but not linked: 9
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben- gio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR.
- Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large an- notated corpus for learning natural language infer- ence. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguis- tics.
- Kyunghyun Cho, Bart van Merrienboer, C ¸aglar Gülc ¸ehre, Dzmitry Bahdanau, Fethi Bougares, Hol- ger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Nat- ural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pages 1724- 1734.
- Alexis Conneau, Ruty Rinott, Guillaume Lample, Ad- ina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov. 2018. Xnli: Evaluating cross- lingual sentence representations. In Proceedings of the 2018 Conference on Empirical Methods in Nat- ural Language Processing. Association for Compu- tational Linguistics.
- Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018. Understanding back-translation at scale. CoRR, abs/1808.09381. · arXiv
- Ramesh Nallapati, Bowen Zhou, C ¸aglar Gülc ¸ehre, Bing Xiang, et al. 2016. Abstractive text summa- rization using sequence-to-sequence rnns and be- yond. Proceedings of SIGNLL Conference on Com- putational Natural Language Learning.
- Ramakanth Pasunuru and Mohit Bansal. 2018. Multi- reward reinforced summarization with saliency and entailment. CoRR, abs/1804.06451. · arXiv
- Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word rep- resentations. In Proc. of NAACL.
- James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: a large-scale dataset for fact extraction and verification. CoRR, abs/1803.05355. · arXiv
Cited by
Sorted by citations
Items 501–600 of 693
Mitigating Societal Harms in Large Language Models
Kumar, S., Balachandran, V., Njoo, L., Anastasopoulos, A., & Tsvetkov, Y. (2023) · doi:10.18653/v1/2023.emnlp-tutorial.5 · Cited by 1
Factual Relation Discrimination for Factuality-oriented Abstractive Summarization
Gao, Z., Li, P., Jiang, F., Chu, X., & Zhu, Q. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.69 · Cited by 1
Improving Consistency for Text Summarization with Energy Functions
Zeng, Q., Yin, Q., Li, Z., Gao, Y., Nag, S., Wang, Z., Yin, B., Ji, H., & Zhang, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.798 · Cited by 1
Optimization of an Abstract Text Summarization Model based on Entity Awareness and Perturbation
Yao, B., & Ding, G. (2023) · International Conference on Electronic Information Engineering and Computer Science (EIECS) · doi:10.1109/eiecs59936.2023.10435599 · Cited by 1
PhraseSumm: Abstractive Short Phrase Summarization
Bhattacharjee, K., McKeown, K. R., & Gangadharaiah, R. (2023) · doi:10.18653/v1/2023.findings-ijcnlp.8 · Cited by 1
Dutta, T., & Liu, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650810 · Cited by 1
Prog-TAPAS: Enabling Table and Program Representation Consistency for Fact Verification
Wang, S., Zhang, C., & Liu, T. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650908 · Cited by 1
Sarawanangkoor, N., Punyabukkana, P., & Suchato, A. (2024) · International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) · doi:10.1109/isai-nlp64410.2024.10799455 · Cited by 1
New Paradigm for Evaluating Scholar Summaries: A Facet-aware Metric and a Meta-evaluation Benchmark
Wang, T., Chen, X., Zhu, Q., Guo, T., Gao, S., Lu, Z., Gao, X., & Zhang, X. (2025) · ACM Transactions on Information Systems · doi:10.1145/3733597 · Cited by 1
Does Summary Evaluation Survive Translation to Other Languages?
Braun, S., Vasilyev, O., Iskender, N., & Bohannon, J. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.173 · Cited by 1
Boosting Few-shot Abstractive Summarization with Auxiliary Tasks
Bi, Q., Li, H., & Yang, H. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482066 · Cited by 1
Identrics, S., Marinova, I., Petrova, Y. A., Identrics, S., Slavcheva, M., Osenova, P., Radev, I., & Simov, K. (2021) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-072-4_103 · Cited by 1
MTMS: A Fact-Corrected Summarization model Based on Multitask Learning and Multimodal Fusion
Xia, Y., Yue, Z., Liu, R., & Lu, Q. (2021) · International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) · doi:10.1109/icbase53849.2021.00051 · Cited by 1
Improving Neural Abstractive Summarization with Reliable Sentence Sampling
Chernyshev, D., & Dobrov, B. V. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-12285-9_16 · Cited by 1
An Algorithmic Approach for Text Summarization
Joshi, A., More, P., Shah, S., & Sahitya, M. A. (2023) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat57137.2023.10080575 · Cited by 1
Evaluating Parameter Efficient Learning for Generation
Xu, P., Patwary, M. A., Prabhumoye, S., Adams, V., Prenger, R., Ping, W., Lee, N., Shoeybi, M., & Catanzaro, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.319 · Cited by 1
Generating Coherent and Diverse Slogans with Sequence-to-Sequence Transformer.Preprint
Jin, Y., Bhatia, A., Wanvarie, D., & Le, P. T. V. (2021) · arXiv (Cornell University) · Cited by 1
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and AttributionPreprint
Xu, J., & Durrett, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01518 · Cited by 1
ComSum: Commit Messages Summarization and Meaning PreservationPreprint
Choshen, L., & Amit, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10763 · Cited by 5
MeetSum: Transforming Meeting Transcript Summarization using Transformers!Preprint
Sadri, N., Zhang, B., & Liu, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06310 · Cited by 1
Automatic sentence simplification using controllable and unsupervised methods
Martin, L. (2021) · theses.fr (ABES) · doi:10.70675/e997dea7zc65ez46ecz924dzc86bd7869f1c · Cited by 1
Espejel, J. L. (2021) · theses.fr (ABES) · doi:10.70675/31684827z4a48z48dfz8dd7zc76a5467c3e8 · Cited by 1
All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs?
Kim, S., Janssen, M. A., Wang, J., Min-Venditti, A., Karanjia, N., & Anderies, J. M. (2026) · arXiv · doi:10.48550/arxiv.2604.17247
Attributable by Construction: Claim-Anchored Provenance for Multi-Document Summarization
Guan, S. (2026) · arXiv · doi:10.48550/arxiv.2606.23989
“Faithfulness evaluation (Maynez et al., 2020; Kryscinski et al., 2020) has moved from n -gram overlap (Lin, 2004) toward entailment-and QA-based consistency metrics (Laban et al., 2022; Fab-bri et al., 2022; Zha et al., 2023; Honovich et al., 2022; Tang et al., 2024) and decomposition into atomic…”
AlignCheck: a Semantic Open-Domain Metric for Factual Consistency Assessment
Aghaebrahimian, A. (2025) · arXiv · doi:10.48550/arxiv.2512.03634
“…factuality evaluation where a model is trained to evaluate the consistency for generated text by formulating it as a binary classification task (Kryscinski et al., 2020), question answering (Deutsch et al., 2021), or Natural Language Inference (NLI) (Laban et al., 2022) through textual…”
HalluHard: A Hard Multi-Turn Hallucination Benchmark
Fan, D., Delsad, S., Flammarion, N., & Andriushchenko, M. (2026) · arXiv · doi:10.48550/arxiv.2602.01031
“This notion is central in summarization and other grounding-sensitive tasks (Kryscinski et al., 2020; He et al., 2025; Bao et al., 2025).”
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module NetworkPreprint
Zhong, W., Tang, D., Feng, Z., Duan, N., Zhou, M., Gong, M., Shou, L., Jiang, D., Wang, J., & Yin, J. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13659
Estimation of Summary-to-Text Inconsistency by Mismatched EmbeddingsPreprint
Vasilyev, O., & Bohannon, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.05156
Keep it Simple: Unsupervised Simplification of Multi-Paragraph TextPreprint
Laban, P., Schnabel, T., Bennett, P. N., & Hearst, M. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03444 · Cited by 5
Summary Explorer: Visualizing the State of the Art in Text SummarizationPreprint
Syed, S., Yousef, T., Al‐Khatib, K., Jänicke, S., & Potthast, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.01879
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 15
Wang, X., Wang, J., Xu, B., Lin, H., Zhang, B., & Yang, Z. (2022) · doi:10.2196/preprints.38052
Tang, M., Cheng, D., Chen, C., Liang, Y., Luo, Y., & Qian, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-00129-1_25
Designing Baseline for Korean Document Summarization using BERT-based Pre-trained Encoder
Park, J., Kim, J.-H., & Lee, H.-C. (2022) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2022.20.6.19
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
Gu, D., On, B.-W., & Jeong, D. (2022) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2022.20.12.25
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
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types
Murugesan, K., Swaminathan, S., Dan, S., Chaudhury, S., Gunasekara, C., Crouse, M., Mahajan, D., Abdelaziz, I., Fokoue, A., Kapanipathi, P., et al. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.274 · Cited by 1
Model Intrinsic Features of Fine-tuning based Text Summarization Models for Factual Consistency
Song, J., Park, N., Hwang, B., Yun, J.-W., Joe, S., Gwon, Y., & Yoon, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.872
BUMP: A Benchmark of Unfaithful Minimal Pairs for Meta-Evaluation of Faithfulness Metrics
Ma, L., Cao, S., Logan, R. L., Lu, D., Ran, S., Zhang, K., Tetreault, J., & Jaimes, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.716 · Cited by 3
Natural language processing for digital advertising
Jin, Y., & Wanvarie, D. (2021) · Chula Digiverse · doi:10.58837/chula.the.2021.121
Lightweight Reference-Less Summary Quality Evaluation via Key Feature Extraction
Zang, S., Zhang, C., Lin, J., Chen, X., & Zhang, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44198-1_41
Nong, Q., Zhang, X., Jin, Z. G., Gao, C., Shang, W., Du, K., Ding, Z., & Ma, Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4600240
A Likelihood Probability-Based Online Summarization Ranking Model
Yue, S., Yu, D., & Xie, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46664-9_23
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
Hashem, T., Wang, W., Wijaya, D. T., Ali, M. E., & Li, Y.-F. (2023) · International Conference on Natural Language Generation · doi:10.18653/v1/2023.inlg-main.8
Evaluating the Summarization Comprehension of Pre-Trained Language Models
Chernyshev, D., & Dobrov, B. V. (2023) · Lobachevskii Journal of Mathematics · doi:10.1134/s1995080223080115
Balepur, N., Huang, J., & Chang, K. C. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.288 · Cited by 1
The student becomes the master: Outperforming GPT3 on Scientific Factual Error Correction
Ashok, D., Kulkarni, A., Pham, H., & Póczos, B. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.451
MuLER: Detailed and Scalable Reference-based Evaluation
Karidi, T., Choshen, L., Patel, G., & Abend, O. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.29
Siledar, T., Banerjee, S., Patil, A. G., Singh, S., Chelliah, M., Garera, N., & Bhattacharyya, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.899 · Cited by 1
Chang, Y.-T., Song, Y.-Z., Chen, Y.-S., & Shuai, H.-H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.707 · Cited by 1
Fact-checking benchmark for the Russian Large Language Models
SberDevices, Kozlova, A., Shevelev, D., & Fenogenova, A. (2023) · Computational Linguistics and Intellectual Technologies · doi:10.28995/2075-7182-2023-22-267-277
Optimization of the Abstract Text Summarization Model Based on Multi-Task Learning
Yao, B., & Ding, G. (2023) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3650400.3650469
GERAÇÃO DE DESCRIÇÕES DE PRODUTOS A PARTIR DE AVALIAÇÕES DE USUÁRIOS USANDO UM LLM
GUTIERREZ, B. F. M. (2024) · doi:10.17771/pucrio.acad.66911
Learning Latent Variable for Logical Reasoning in Table-Based Fact Verification
Wu, J., & Hou, M. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651044
An Abstractive Approach for Gujarati Text Summarization
Tailor, V., & Tanna, P. (2024) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-97-5862-3_10
Abstract Summarization Model Based on Semantic Graphs and Entity Pointers
Zhao, J., & Zhu, C. (2024) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp62122.2024.10743306
Aspect-Adaptive Knowledge-based Opinion Summarization
Wang, G., Li, W., Lai, E. M.-K., & Bai, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0026-7_3
A Knowledge Graph Summarization Model Integrating Attention Alignment and Momentum Distillation
Wang, Z., & Zhao, X. (2025) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2025.p0205
Enhancing Generative Models for Scientific Text Simplification
Vendeville, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88720-8_39 · Cited by 1
Transformer-Based Model for High-Quality Text Summarization
Karteek, V. V., Pranav, P. V. S., Charan, V. S., Swaraja, K., Vardhini, P. A. H., & Aouthu, S. (2025) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict64420.2025.11004690
Chen, D., Hu, D., & Li, L. (2024) · International Conference on Communication and Information Processing (ICCIP) · doi:10.1145/3708657.3708772
APG: Automatic Prompt Generation for Improved Document Summarization
Parnell, J., Unanue, I. J., Matthews, S. L., & Piccardi, M. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3577363
Herserant, T., & Guigue, V. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8186-0_11
RAG Architecture Design Patterns Balancing Retrieval Depth and Generative Coherence
Muniyandi, V. (2025) · International Journal of Computer Applications · doi:10.5120/ijca2025925142
Methods to Evaluate and Improve the Quality of Text Summarization
Nguyen, H. (2025) · University of North Texas Digital Library (University of North Texas) · doi:10.12794/metadc2443390
Gupta, K., Saxena, J., Saxena, P., Kumar, M., & Chauhan, P. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11070502
Chen, D., Hu, D., & Li, L. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_33
A Table Fact Verification Framework Based on Integrating Vocabulary and Deep Semantic Information
Wang, Y., Wang, X., Gao, Q., & Shao, L.-H. (2025) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd64986.2025.11081984
He, Y., Li, G., Liu, F., & Yin, S. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-93251-9_21
Improving Abstractive News Summarization Using Keyword Extraction for Human-Like Summaries
Adjie, M. L., Pribadi, W. G., Wulandhari, L. A., & Nabiilah, G. Z. (2025) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict65714.2025.11101587
Detecting Hallucinations in Textual Data: Insights From Machine Learning Models in Contextual QA
Umrao, S., Parashar, A., Chaubey, V. P., Sharma, S., & Ashfaq, F. (2024) · IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS) · doi:10.1109/icetas62372.2024.11120287
Entity hallucination index for text summarization models
Katwe, P., Balabantaray, R. C., & Vittala, K. P. (2025) · Applied Computing and Informatics · doi:10.1108/aci-09-2024-0371
Intent-Driven Task-Oriented Resource Collaboration for Cloud-Edge-End Vehicular Networks
Huang, J., Chi, K., Yang, C., & Han, Z. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3617931
Hierarchical Attention-Based GAN with Transformer for Abstractive Text Summarisation
Swetha, G., & Phanikumar, S. (2025) · Journal of Information & Knowledge Management · doi:10.1142/s0219649225500984
Ashour, S. (2025) · The political economy of the Middle East/The political economy of the Middle East · doi:10.1007/978-981-96-7963-8_4
Bello-Orgaz, G., Dakhama, A., Ramirez‐Atencia, C., & Menéndez, H. D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5593064
Improving extractive text summarization via efficient Coati Algorithm for single document
Rautaray, J., Panigrahi, S., & Nayak, A. K. (2025) · Facta universitatis - series Electronics and Energetics · doi:10.2298/fuee2503533r
Towards Trustworthy Conversational Agents: A New Method for Hallucination Detection in NLG
Ghani, S., Håkansson, A., & Pasichnyi, O. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.651
Weerasinghe, C. D. R. M., Gunasinghe, M. R. A. A. K., Siriwardana, H. B. K. S., & Perera, I. (2025) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon67903.2025.11217068
MIRAGE: A Metrics lIbrary for Rating hAllucinations in Generated tExt
Vendeville, B., Магдановна, Е. Л., Loor, P. D., & Kamps, J. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761644 · Cited by 2
Towards Trustworthy AI: Enhancing Factuality, Bias, and Compliance in LLMs
Amatya, P. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761664
Intent-aware personalized summarization for educational texts with large language models
Ning, W., Shi, S., Yin, Y., Guo, Z., & Xie, J. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00333-7 · Cited by 1
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
Hao, X., Li, Y., & Li, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_10
Gupta, A., Matta, P., & Pant, B. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02872-8
Training LLM with Human Feedback
Rezaei, H., Beheshti, A., & Rabhi, F. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_53
Triple-based Factual Correctness Evaluation of AI-Generated Summaries
Latipov, I.-A., Holenderski, M., & Meratnia, N. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.12.107
Lymperaiou, M., Filandrianos, G., Dimitriou, A., Voulodimos, A., & Stamou, G. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08330-2_5
MAD-HD: Multi-agent Debate-Driven Ungrounded Hallucination Detection
Gao, Z., Bao, F., & Li, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3343-5_39
Program-Guided Refinement with Debate: A Multi-Agent LLM-Based Automated Fact-Checking ModelPreprint
Xue, T., Liu, W., Xi, L., & Lv, W. (2025) · Research Square · doi:10.21203/rs.3.rs-8033646/v1
Exploring Extractive and Abstractive Summarization Methods in Indonesian Text: A Systematic Survey
Rohman, A. N., Utami, E., Kusrini, K., & Muhammad, A. H. (2025) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris67789.2025.11296075
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 11
Prompt Recovery for Misinformation Detection at FIRE 2025
Mehta, P., Hegde, A., Shahi, G. K., Satapara, S., Modha, S., Ganguly, D., Nandini, D., Shashirekha, H. L., Jaiswal, A. K., Pasi, G., et al. (2025) · Annual Meeting of the Forum for Information Retrieval Evaluation (FIRE) · doi:10.1145/3777867.3778261
Katwe, P., Balabantaray, R. C., & Vittala, K. P. (2026) · SN Computer Science · doi:10.1007/s42979-025-04684-w
Part-of-Speech Prompting for Chunking-Based Factual Error Correction
Deng, Z., & Jiang, S. (2025) · International Conference on Digital Society and Intelligent Systems (DSInS) · doi:10.1109/dsins68311.2025.11329866
Wang, G., & Shuang, K. (2025) · International Conference on Machine Learning and Computer Application (ICMLCA) · doi:10.1109/icmlca66850.2025.11336683
Greek Automatic Text Summarization: From General Language Models to Legal Domain Challenges
Αθανασίου, Π., Karvelis, P. S., & Stylios, C. D. (2025) · South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) · doi:10.1109/seeda-cecnsm68644.2025.11329930
Metadata
- Authors
- Kryscinski, W., McCann, B., Xiong, C., & Socher, R.
- Affiliations
- Salesforce (United States) (US)
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 9332-9346
- OpenAlex
- W2981456979
- Cited by
- 669
- References
- 41
- Citations by year
- 2026: 94 · 2025: 86 · 2024: 56 · 2023: 171 · 2022: 106 · 2021: 118 · 2020: 37
- FWCI
- 41.26
- 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)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- abstractive summarization, factual consistency, weakly supervised learning, rule-based transformation, span extraction, natural language inference, fact-checking, summarization evaluation metrics
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
