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FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
Open accessAbstract
Neural abstractive summarization models are prone to generate content inconsistent with the source document, i.e. unfaithful.Existing automatic metrics do not capture such mistakes effectively.We tackle the problem of evaluating faithfulness of a generated summary given its source document.We first collected human annotations of faithfulness for outputs from numerous models on two datasets.We find that current models exhibit a trade-off between abstractiveness and faithfulness: outputs with less word overlap with the source document are more likely to be unfaithful.Next, we propose an automatic question answering (QA) based metric for faithfulness, FEQA, 1 which leverages recent advances in reading comprehension.Given questionanswer pairs generated from the summary, a QA model extracts answers from the document; non-matched answers indicate unfaithful information in the summary.Among metrics based on word overlap, embedding similarity, and learned language understanding models, our QA-based metric has significantly higher correlation with human faithfulness scores, especially on highly abstractive summaries.* Most of the work is done while the authors were at Amazon Web Services AI.1 Faithfulness Evaluation with Question Answering.
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“A similar phenomenon has been shown in other NLG tasks, where it has been discovered that traditional metrics have a poor correlation with human judgment in terms of the hallucination problem [45, 54, 101, 125].”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [54]
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“The results show that these reference-free metrics have substantially higher correlations with human judgments of faithfulness than the baseline metrics.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [54]
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“The extracted answers that do not match the provided answers indicate unfaithful information in the summary.”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [54]
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“In contrast to classifier-based metrics, QA-based metrics [77, 119, 271, 310] have recently garnered attention for their enhanced ability to capture information overlap between the model's generation and its source.”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [77]
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“Their approach is based on the intuition that if a summary is consistent with its source, questions asked on the summary and the source should result in similar answers.”
From Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021) · cites this paper as Durmus et al. (2020)
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“Durmus et al. (2020) and Wang et al. (2020) suggested using QG and QA to identify factual inconsistencies in abstractive summaries, which is more Precisionoriented.”
From Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021) · cites this paper as Durmus et al. (2020)
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“The guidelines are based on the insights provided byDurmus et al. (2020) regarding annotating faithfulness.”
From Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering (2021) · cites this paper as Durmus et al. (2020)
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GO FIGURE: A Meta Evaluation of Factuality in Summarization
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Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation
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TRUE: Re-evaluating Factual Consistency Evaluation
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FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization
Wan, D., & Bansal, M. (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.74 · Cited by 66
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
Xu, W., Agrawal, S., Briakou, E., Martindale, M. J., & Carpuat, M. J. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00563 · Cited by 66
Intrinsic Evaluation of Summarization Datasets
Bommasani, R., & Cardie, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.649 · Cited by 60
QuALITY: Question Answering with Long Input Texts, Yes!
Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.391 · Cited by 51
Self-citationROUGE-SEM: Better evaluation of summarization using ROUGE combined with semantics
Zhang, M., Li, C., Wan, M., Zhang, X., & Zhao, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121364 · Cited by 61
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors
Tang, L., Goyal, T., Fabbri, A. R., Laban, P., Xu, J., Yavuz, S., Kryściński, W., Rousseau, J. F., & Durrett, G. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.650 · Cited by 87
Deng, M., Tan, B., Liu, Z., Xing, E. P., & Hu, Z. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.599 · Cited by 60
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark
Dziri, N., Rashkin, H., Linzen, T., & Reitter, D. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00506 · Cited by 79
Ladhak, F., Durmus, E., He, H., Cardie, C., & McKeown, K. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.100 · Cited by 46
Self-citationFew-Shot Bot: Prompt-Based Learning for Dialogue SystemsPreprint
Madotto, A., Lin, Z., Winata, G. I., & Fung, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.08118 · Cited by 96
What’s in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
Adams, G., Alsentzer, E., Ketenci, M., Zucker, J. E., & Elhadad, N. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.382 · Cited by 57
All You May Need for VQA are Image Captions
Changpinyo, S., Kukliansy, D., Szpektor, I., Chen, X., Ding, N., & Soricut, R. (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.142 · Cited by 67
Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation
Xie, Y., Sun, F., Deng, Y., Li, Y., & Ding, B. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.10 · Cited by 49
Factual consistency evaluation of summarization in the Era of large language models
Luo, Z., Xie, Q., & Ananiadou, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124456 · Cited by 59
Evaluating Factuality in Text Simplification
Devaraj, A., Sheffield, W., Wallace, B., & Li, J. J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.506 · Cited by 40
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
Ribeiro, L., Liu, M., Gurevych, I., Dreyer, M., & Bansal, M. (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.236 · Cited by 54
Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures
Bagdasaryan, E., & Shmatikov, V. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833572 · Cited by 64
Focus Attention: Promoting Faithfulness and Diversity in Summarization
Aralikatte, R., Narayan, S., Maynez, J., Rothe, S., & McDonald, R. (2021) · doi:10.18653/v1/2021.acl-long.474 · Cited by 45
RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Ji, Z., Liu, Z., Lee, N., Yu, T., Wilie, B., Zeng, M., & Fung, P. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.275 · Cited by 37
Understanding Neural Abstractive Summarization Models via Uncertainty
Xu, J., Desai, S., & Durrett, G. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.508 · Cited by 43
Generating Literal and Implied Subquestions to Fact-check Complex Claims
Chen, J., Sriram, A., Choi, E., & Durrett, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.229 · Cited by 34
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Su, D., Li, X., Zhang, J., Shang, L., Jiang, X., Liu, Q., & Fung, P. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.61 · Cited by 37
Balachandran, V., Hajishirzi, H., Cohen, W. W., & Tsvetkov, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.667 · Cited by 33
EmailSum: Abstractive Email Thread Summarization
Zhang, S., Çelikyılmaz, A., Gao, J., & Bansal, M. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.537 · Cited by 31
When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization
Ladhak, F., Durmus, E., Suzgun, M., Zhang, T., Jurafsky, D., McKeown, K., & Hashimoto, T. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.234 · Cited by 30
Self-citationLearning to Revise References for Faithful Summarization
Adams, G., Shing, H.-C., Sun, Q., Winestock, C., McKeown, K. R., & Elhadad, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.296 · Cited by 34
Multilingual Summarization with Factual Consistency Evaluation
Aharoni, R., Narayan, S., Maynez, J., Herzig, J., Clark, E., & Lapata, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.220 · Cited by 47
Shin, J., Yu, H., Moon, H., Madotto, A., & Park, J. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.302 · Cited by 31
WiCE: Real-world entailment for claims in Wikipedia
Kamoi, R., Goyal, T., Rodriguez, J., & Durrett, G. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.470 · Cited by 26
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Zhang, H., Yavuz, S., Kryściński, W., Hashimoto, K., & Zhou, Y. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.40 · Cited by 31
Utama, P. A., Bambrick, J., Moosavi, N. S., & Gurevych, I. (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.199 · Cited by 33
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Roit, P., Ferret, J., Shani, L., Aharoni, R., Cideron, G., Dadashi, R., Geist, M., Girgin, S., Hussenot, L., Keller, O., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.344 · Cited by 63
DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence
Zhao, W., Strube, M., & Eger, S. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.278 · Cited by 44
On the Opacity of Deep Neural Networks
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Conditional Generation with a Question-Answering Blueprint
Narayan, S., Maynez, J., Amplayo, R. K., Ganchev, K., Louis, A. P., Huot, F., Sandholm, A., Das, D., & Lapata, M. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00583 · Cited by 30
An Investigation of Evaluation Methods in Automatic Medical Note Generation
Abacha, A. B., Yim, W., Michalopoulos, G., & Lin, T. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.161 · Cited by 47
QuestEval: Summarization Asks for Fact-based EvaluationPreprint
Scialom, T., Dray, P.-A., Gallinari, P., Lamprier, S., Piwowarski, B., Staiano, J., & Wang, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12693 · Cited by 22
Opportunities and Risks of LLMs for Scalable Deliberation with Polis
Small, C. T., Vendrov, I., Durmus, E., Homaei, H., Barry, E., Cornebise, J., Suzman, T., Ganguli, D., & Megill, C. (2023) · arXiv · doi:10.48550/arxiv.2306.11932 · Cited by 20
“Prior work has proposed automated metrics to detect such errors (Maynez et al., 2020; Durmus et al., 2020; Kryscinski et al., 2020; Goyal and Durrett, 2021); however, they do not always correlate well with human judgements (Fabbri et al., 2021; Durmus et al., 2022).”
BackgroundSelf-citationSQuALITY: Building a Long-Document Summarization Dataset the Hard Way
Wang, A., Pang, R. Y., Chen, A., Phang, J., & Bowman, S. R. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.75 · Cited by 25
Understanding Points of Correspondence between Sentences for Abstractive Summarization
Lebanoff, L., Muchovej, J., Dernoncourt, F., Kim, D. S., Wang, L., Chang, W. H., & Liu, F. (2020) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/2020.acl-srw.26 · Cited by 23
FFCI: A Framework for Interpretable Automatic Evaluation of Summarization
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Question Answering Survey: Directions, Challenges, Datasets, Evaluation MatricesPreprint
Pandya, H. A., & Bhatt, B. (2021) · Preprints.org · doi:10.20944/preprints202112.0136.v1 · Cited by 20
Mutlu, B., & Sezer, E. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120302 · Cited by 19
Evaluation metrics on text summarization: comprehensive survey
Davoodijam, E., & Meybodi, M. A. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02217-0 · Cited by 23
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization
Chen, Y., Liu, P., & Qiu, X. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.179 · Cited by 21
Automated Metrics for Medical Multi-Document Summarization Disagree with Human Evaluations
Wang, L. L., Otmakhova, Y., DeYoung, J., Truong, T. H., Kuehl, B., Bransom, E., & Wallace, B. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.549 · Cited by 22
Zhang, S., Wan, D., & Bansal, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.120 · Cited by 33
How Far are We from Robust Long Abstractive Summarization?
Koh, H. Y., Ju, J., Zhang, H., Liu, M. Z., & Pan, S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.172 · Cited by 21
Compressive Summarization with Plausibility and Salience Modeling
Desai, S., Xu, J., & Durrett, G. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.507 · Cited by 17
DialSummEval: Revisiting Summarization Evaluation for Dialogues
Gao, M., & Wan, X. (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.418 · Cited by 20
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias
Lee, N., Bang, Y., Yu, T., Madotto, A., & Fung, P. (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.228 · Cited by 26
Using Question Answering Rewards to Improve Abstractive Summarization
Gunasekara, C., Feigenblat, G., Sznajder, B., Aharonov, R., & Joshi, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.47 · Cited by 17
Metadata
- Authors
- Durmus, E., He, H., & Diab, M.
- Affiliations
- Cornell University (US)
- George Washington University (US)
- ORCID
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 5055-5070
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3022685184
- Cited by
- 306
- References
- 1
- Citations by year
- 2026: 20 · 2025: 28 · 2024: 28 · 2023: 75 · 2022: 68 · 2021: 71 · 2020: 16
- FWCI
- 21.97
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Amazon Web Services
- 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, lexical overlap, embedding similarity, reading comprehension
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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