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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.
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“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
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“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
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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]
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“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]
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“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
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“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]
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“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]
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“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
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Cites
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Hallucination Detection Middleware using RAG Based Model
Lakshmanan, A., Nithyanandh, S., & Mohanapriya, D. (2026) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2612556
Estimating Hallucination Risk in Large Language Models with Intrinsic Priors and Entailment Signals
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Controlling Attention Dynamics for Factual Grounding in Neural Text Generation
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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 ›
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