QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

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 48,293 · see citing works

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

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 · see citing works

The Jackknife, the Bootstrap and Other Resampling Plans

Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120 · see citing works

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · see citing works

HuggingFace's Transformers: State-of-the-art Natural Language Processing

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.03771 · Cited by 3,347 · see citing works

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 · see citing works

BERTScore: Evaluating Text Generation with BERT

Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865 · see citing works

Teaching Machines to Read and ComprehendPreprint

Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,779 · see citing works

Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

Nallapati, R., Zhou, B., dos Santos, C., Gulcehre, C., & Xiang, B. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1028 · Cited by 2,361 · see citing works

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.10555 · Cited by 1,844 · see citing works

Text Summarization with Pretrained Encoders

Liu, Y., & Lapata, M. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1387 · Cited by 1,779 · see citing works

Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Narayan, S., Cohen, S. B., & Lapata, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1206 · Cited by 1,207 · see citing works

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

Zhang, J., Zhao, Y., Saleh, M., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1912.08777 · Cited by 1,206 · see citing works

On Faithfulness and Factuality in Abstractive Summarization

Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.173 · Cited by 1,185 · see citing works

PyTorch distributed

Shen, L., Zhao, Y., Varma, R., Salpekar, O., Noordhuis, P., Li, T., Paszke, A., Smith, J. S., Vaughan, B., Damania, P., et al. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3415478.3415530 · Cited by 684 · see citing works

Evaluating the Factual Consistency of Abstractive Text Summarization

Kryscinski, W., McCann, B., Xiong, C., & Socher, R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.750 · Cited by 669 · see citing works

Adversarial NLI: A New Benchmark for Natural Language Understanding

Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.441 · Cited by 656 · see citing works

SummEval: Re-evaluating Summarization Evaluation

Fabbri, A. R., Kryściński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00373 · Cited by 587 · see citing works

UNIFIEDQA: Crossing Format Boundaries with a Single QA System

Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., & Hajishirzi, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.171 · Cited by 514 · see citing works

Il calcolo delle assicurazioni su gruppi di teste

Bonferroni, C., & Bonferroni, C. (1935) · Medical Entomology and Zoology · Cited by 425 · see citing works

Untitled work

(2020) · doi:10.18653/v1/2020.acl-main.450 · Cited by 356 · see citing works

BARTScore: Evaluating Generated Text as Text Generation

Yuan, W., Neubig, G., & Liu, P. (2021) · arXiv · doi:10.48550/arxiv.2106.11520 · Cited by 353 · see citing works

Neural Text Summarization: A Critical Evaluation

Kryscinski, W., Keskar, N. S., McCann, B., Xiong, C., & Socher, R. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1051 · Cited by 335 · see citing works

FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization

Durmus, E., He, H., & Diab, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.454 · Cited by 306 · see citing works

Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference

Falke, T., Ribeiro, L. F. R., Utama, P. A., Dagan, I., & Gurevych, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1213 · Cited by 265 · see citing works

Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics

Pagnoni, A., Balachandran, V., & Tsvetkov, Y. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.383 · Cited by 197 · see citing works

Transforming Question Answering Datasets Into Natural Language Inference DatasetsPreprint

Demszky, D., Guu, K., & Liang, P. (2018) · arXiv.org · doi:10.48550/arxiv.1809.02922 · Cited by 192 · see citing works

QuestEval: Summarization Asks for Fact-based Evaluation

Scialom, T., Dray, P.-A., Lamprier, S., Piwowarski, B., Staiano, J., Wang, A., & Gallinari, P. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.529 · Cited by 175 · see citing works

Re-evaluating Evaluation in Text Summarization

Bhandari, M., Gour, P. N., Ashfaq, A., Liu, P., & Neubig, G. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.751 · Cited by 148 · see citing works

Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence

Schuster, T., Fisch, A., & Barzilay, R. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.52 · Cited by 126 · see citing works

Answers Unite! Unsupervised Metrics for Reinforced Summarization Models

Scialom, T., Lamprier, S., Piwowarski, B., & Staiano, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1320 · Cited by 121 · see citing works

Evaluating Factuality in Generation with Dependency-level Entailment

Goyal, T., & Durrett, G. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.322 · Cited by 93 · see citing works

Question Answering as an Automatic Evaluation Metric for News Article Summarization

Eyal, M., Baumel, T., & Elhadad, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1395 · Cited by 92 · see citing works

Fill in the BLANC: Human-free quality estimation of document summaries

Vasilyev, O., Dharnidharka, V., & Bohannon, J. (2020) · Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) · doi:10.18653/v1/2020.eval4nlp-1.2 · Cited by 84 · see citing works

What Have We Achieved on Text Summarization?

Huang, D., Cui, L., Yang, S., Bao, G., Wang, K., Xie, J., & Zhang, Y. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.33 · Cited by 80 · see citing works

GO FIGURE: A Meta Evaluation of Factuality in Summarization

Gabriel, S., Celikyilmaz, A., Jha, R., Choi, Y., & Gao, J. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.42 · Cited by 75 · see citing works

Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary

Deutsch, D., Bedrax-Weiss, T., & Roth, D. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00397 · Cited by 75 · see citing works

DocNLI: A Large-scale Dataset for Document-level Natural Language Inference

Yin, W., Radev, D., & Xiong, C. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.435 · Cited by 63 · see citing works

SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

Laban, P., Schnabel, T., Bennett, P. N., & Hearst, M. A. (2021) · arXiv · doi:10.48550/arxiv.2111.09525 · Cited by 38 · see citing works

MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics

Chen, A., Stanovsky, G., Singh, S., & Gardner, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.528 · Cited by 33 · see citing works

MixQG: Neural Question Generation with Mixed Answer Types

Murakhovs’ka, L., Wu, C.-S., Laban, P., Niu, T., Liu, W., & Xiong, C. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.111 · Cited by 31 · see citing works

SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics

Deutsch, D., & Roth, D. (2020) · NLPOSS · doi:10.18653/v1/2020.nlposs-1.17 · Cited by 29 · see citing works

Understanding the Extent to which Content Quality Metrics Measure the Information Quality of Summaries

Deutsch, D., & Roth, D. (2021) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2021.conll-1.24 · Cited by 26 · see citing works

Question Answering Infused Pre-training of General-Purpose Contextualized Representations

Jia, R., Lewis, M., & Zettlemoyer, L. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.59 · Cited by 23 · see citing works

FFCI: A Framework for Interpretable Automatic Evaluation of Summarization

Koto, F., Baldwin, T., & Lau, J. H. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13167 · Cited by 19 · see citing works

Truth or Error? Towards systematic analysis of factual errors in abstractive summaries

Lux, K.-M., Sappelli, M., & Larson, M. (2020) · Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) · doi:10.18653/v1/2020.eval4nlp-1.1 · Cited by 14 · see citing works

Adversarial NLI for Factual Correctness in Text Summarisation Models

Barrantes, M., Herudek, B., & Wang, R. (2020) · arXiv · doi:10.48550/arxiv.2005.11739 · Cited by 10 · see citing works

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 2,142 · see citing works

Benchmarking Large Language Models for News Summarization

Zhang, T., Ladhak, F., Durmus, E., Liang, P., McKeown, K., & Hashimoto, T. B. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00632 · Cited by 415 · see citing works

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.741 · Cited by 407 · see citing works

A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2023) · arXiv · doi:10.48550/arxiv.2311.05232 · Cited by 2,516 · see citing works

Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior

Anh-Hoang, D., Tran, V., & Nguyen, L.-M. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1622292 · Cited by 146 · see citing works

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text

Gehrmann, S., Clark, E., & Sellam, T. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13715 · Cited by 123 · see citing works

AlignScore: Evaluating factual consistency with a unified alignment function

Zha, Y., Yang, Y., Li, R., & Hu, Z. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.634 · Cited by 81 · see citing works

TRUE: Re-evaluating Factual Consistency Evaluation

Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (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.287 · Cited by 75 · see citing works

Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation

Liu, Y., Fabbri, A. R., Liu, P., Zhao, Y., Nan, L., Han, R., Han, S., Joty, S., Wu, C.-S., Xiong, C., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.228 · Cited by 75 · see citing works

ROUGE-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 54 · see citing works

Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation

Bulian, J., Buck, C., Gajewski, W., Börschinger, B., & Schuster, T. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.20 · Cited by 44 · see citing works

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 48 · see citing works

SummEdits: Measuring LLM Ability at Factual Reasoning Through The Lens of Summarization

Laban, P., Kryściński, W., Agarwal, D., Fabbri, A. R., Xiong, C., Joty, S., & Wu, C.-S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.600 · Cited by 34 · see citing works

A Critical Evaluation of Evaluations for Long-form Question Answering

Xu, F., Song, Y., Iyyer, M., & Choi, E. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.181 · Cited by 33 · see citing works

Beyond the Chat: Executable and Verifiable Text-Editing with LLMs

Laban, P., Vig, J., Hearst, M., Xiong, C., & Wu, C.-S. (2024) · Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3654777.3676419 · Cited by 31 · see citing works

A Field Guide to Automatic Evaluation of LLM-Generated Summaries

van Schaik, T. A., & Pugh, B. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3661346 · Cited by 30 · see citing works

LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization

Krishna, K., Bransom, E., Kuehl, B., Iyyer, M., Dasigi, P., Cohan, A., & Lo, K. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.121 · Cited by 29 · see citing works

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 36 · see citing works

FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios

Chern, I.-C., Chern, S., Chen, S., Yuan, W., Feng, K., Zhou, C., He, J., Neubig, G., & Liu, P. (2023) · arXiv · doi:10.48550/arxiv.2307.13528 · Cited by 29 · see citing works

Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool

Chen, X., Wu, C.-S., Murakhovs’ka, L., Laban, P., Niu, T., Liu, W., & Xiong, C. (2023) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3609331 · Cited by 29 · see citing works

Zero-shot Faithful Factual Error Correction

Huang, K.-H., Chan, H. P., & Ji, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.311 · Cited by 18 · see citing works

Detecting and Mitigating Hallucinations in Multilingual Summarisation

Qiu, Y., Ziser, Y., Korhonen, A., Ponti, E. M., & Cohen, S. B. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.551 · Cited by 21 · see citing works

Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers

Subbiah, M., Zhang, S., Chilton, L. B., & McKeown, K. R. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00702 · Cited by 26 · see citing works

Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters

Schuster, T., Chen, S., Buthpitiya, S., Fabrikant, A., & Metzler, D. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.28 · Cited by 17 · see citing works

Towards Dataset-Scale and Feature-Oriented Evaluation of Text Summarization in Large Language Model Prompts

Lee, S. Y.-T., Bahukhandi, A., Liu, D., & Ma, K. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456398 · Cited by 18 · see citing works

FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence

Joseph, S., Chen, L., Trienes, J., Göke, H., Coers, M., Xu, W., Wallace, B., & Li, J. J. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.459 · Cited by 17 · see citing works

RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question

Mohammadshahi, A., Scialom, T., Yazdani, M., Yanki, P., Fan, A., Henderson, J., & Saeidi, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.428 · Cited by 24 · see citing works

Improving Factual Consistency in Summarization with Compression-Based Post-Editing

Fabbri, A. R., Choubey, P. K., Vig, J., Wu, C.-S., & Xiong, C. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.623 · Cited by 19 · see citing works

CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization

Choubey, P. K., Fabbri, A. R., Vig, J., Wu, C.-S., Liu, W., & Rajani, N. F. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.685 · Cited by 23 · see citing works

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-T., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023) · arXiv · doi:10.48550/arxiv.2305.14251 · Cited by 14 · see citing works

Improving Factuality of Abstractive Summarization via Contrastive Reward Learning

Chern, I.-C., Wang, Z., Das, S. K., Sharma, B., Liu, P., & Neubig, G. (2023) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2023.trustnlp-1.6 · Cited by 18 · see citing works

FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge

Feng, S., Balachandran, V., Bai, Y., & Tsvetkov, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.59 · Cited by 21 · see citing works

Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension

Agrawal, S., & Carpuat, M. J. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00653 · Cited by 17 · see citing works

MedKA: A knowledge graph-augmented approach to improve factuality in medical Large Language Models

Deng, Y., Zhao, S., Miao, Y., Zhu, J., & Li, J. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104871 · Cited by 14 · see citing works

On Improving Summarization Factual Consistency from Natural Language Feedback

Liu, Y., Deb, B., Teruel, M., Halfaker, A., Radev, D., & Awadallah, A. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.844 · Cited by 17 · see citing works

Automatic Multilingual Question Generation for Health Data Using LLMs

Ackerman, R., & Balyan, R. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7587-7_1 · Cited by 12 · see citing works

FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization

Zhang, N., Zhang, Y., Guo, W., Mitra, P., & Zhang, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.673 · Cited by 15 · see citing works

A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization

Adams, G., Zucker, J. E., & Elhadad, N. (2023) · PubMed · doi:10.48550/arxiv.2303.03948 · Cited by 15 · see citing works

Attributable and Scalable Opinion Summarization

Hosking, T., Tang, H., & Lapata, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.473 · Cited by 14 · see citing works

A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models

Yang, F., Liu, X. C., Lu, L., Wang, B., & Liu, C. (2025) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981251322468 · Cited by 10 · see citing works

Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization

Chan, H. P., Zeng, Q., & Ji, H. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.402 · Cited by 8 · see citing works

Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems

Murakhovs’ka, L., Laban, P., Xie, T., Xiong, C., & Wu, C.-S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.657 · Cited by 7 · see citing works

Comprehensive Evaluation of AI Hallucination and Novel UV-Oriented Framework toward Safe and Trustworthy AI

Liu, Z., Kou, J., Zhang, W., Gu, C., Fang, X., Huang, Z., Hao, Y., Li, H., Lu, X., Yin, A., et al. (2024) · International Conference on Universal Village (UV) · doi:10.1109/uv63228.2024.11189137 · Cited by 9 · see citing works

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization

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Expository Text Generation: Imitate, Retrieve, Paraphrase

Balepur, N., Huang, J., & Chang, K. C. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.729 · Cited by 12 · see citing works

LENS: Layers of Evaluation of Hallucination in GenAI Systems

Gu, C., Zhang, W., Huang, Z., Kou, J., Liu, Z., Zhao, C., Liu, C., Zhang, L., Lin, W., Wang, Z., et al. (2024) · International Conference on Universal Village (UV) · doi:10.1109/uv63228.2024.11189150 · Cited by 16 · see citing works

Ethical perspectives on deployment of large language model agents in biomedicine: a survey

Nia, N. G., Amiri, A., Luo, Y., & Kline, A. S. (2025) · AI and Ethics · doi:10.1007/s43681-025-00847-w · Cited by 7 · see citing works

Fast and Accurate Factual Inconsistency Detection Over Long Documents

Lattimer, B. M., Chen, P. M., Zhang, X., & Yang, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.105 · Cited by 10 · see citing works

Evaluating and Improving Factuality in Multimodal Abstractive Summarization

Wan, D., & Bansal, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.654 · Cited by 8 · see citing works

Factuality Guided Diffusion-Based Abstractive Summarization

Shin, J., Park, H., & Song, H.-J. (2024) · IEEE Access · doi:10.1109/access.2024.3467063 · Cited by 5 · see citing works

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 5 · see citing works

Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization

Cheang, C., Chan, H. P., Wong, D. F., Liu, X., Li, Z., Sun, Y., Liu, S., & Chao, L. S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1007 · Cited by 6 · see citing works

Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive SummarizationPreprint

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 · see citing works

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 · see citing works

Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework

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 · see citing works

Aligning Factual Consistency for Clinical Studies Summarization through Reinforcement Learning

Tang, X., Cohan, A., & Gerstein, M. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.7 · Cited by 4 · see citing works

WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning

Wu, W., Li, W., Xiao, X., Liu, J., Li, S., & Lyu, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.18 · Cited by 7 · see citing works

Concise Answers to Complex Questions: Summarization of Long-form Answers

Potluri, A., Xu, F., & Choi, E. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.541 · Cited by 5 · see citing works

Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation

Liu, Y., Fabbri, A. R., Zhao, Y., Liu, P., Joty, S., Wu, C.-S., Xiong, C., & Radev, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1018 · Cited by 8 · see citing works

DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4

Hu, Y., Song, K., Cho, S., Wang, X., Foroosh, H., & Liu, F. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.519 · Cited by 5 · see citing works

Understanding Faithfulness and Reasoning of Large Language Models on Plain Biomedical Summaries

Fang, B., Dai, X., & Karimi, S. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.578 · Cited by 4 · see citing works

Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-Generation Cloze

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 · see citing works

Walking a Tightrope – Evaluating Large Language Models in High-Risk Domains

Hung, C.-C., Rim, W. B., Frost, L., Bruckner, L., & Lawrence, C. (2023) · GenBench Workshop on Generalisation (Benchmarking) in NLP (GenBench) · doi:10.18653/v1/2023.genbench-1.8 · Cited by 5 · see citing works

CASESUMM: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court OpinionsPreprint

Heddaya, M., MacMillan, K., Malani, A., Mei, H., & Tan, C. (2025) · Qeios · doi:10.32388/ej9r29 · Cited by 3 · see citing works

A comprehensive study on factual consistency in LLMs for data-to-text generation

Mahapatra, J., & Garain, U. (2025) · Sadhana · doi:10.1007/s12046-025-02860-5 · Cited by 4 · see citing works

USB: A Unified Summarization Benchmark Across Tasks and Domains

Krishna, K., Gupta, P., Ramprasad, S., Wallace, B., Bigham, J. P., & Lipton, Z. C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.592 · Cited by 2 · see citing works

Discord Questions: A Computational Approach To Diversity Analysis in News Coverage

Laban, P., Wu, C.-S., Murakhovs’ka, L., Chen, X., & Xiong, C. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.380 · Cited by 5 · see citing works

FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction

Scirè, A., Ghonim, K., & Navigli, R. (2024) · arXiv · doi:10.48550/arxiv.2403.02270 · Cited by 2 · see citing works

Bayesian Calibration of Win Rate Estimation with LLM Evaluators

Gao, Y., Xu, G., Wang, Z., & Cohan, A. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.273 · Cited by 1 · see citing works

Visual Storytelling with Question-Answer Plans

Liu, D., Lapata, M., & Keller, F. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.386 · Cited by 3 · see citing works

S-Evaluator: Enhance Factual Consistency Evaluator with Adversarial Data Synthesized by Large Language Model

Liu, J., Du, W., Li, Q., Wang, X., Zhou, Z., & Liu, J. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448276 · Cited by 1 · see citing works

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 · see citing works

Mind the Link: Discourse Link-Aware Hallucination Detection in Summarization

Lee, D., Jung, H., & Choi, Y. S. (2025) · Applied Sciences · doi:10.3390/app151910506 · Cited by 1 · see citing works

Language Models for Automated Market Commentary from Corporate Disclosures

Assis, G., Dutra, H., Vianna, D., Meira, W., da Silva, A. S., & Paes, A. (2025) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3768292.3770438 · Cited by 1 · see citing works

Quantum-Inspired Cross-Attention Alignment for Turkish Scientific Abstractive Summarization

ALTAY, G., & Küçüksille, E. U. (2025) · Electronics · doi:10.3390/electronics14224474 · Cited by 1 · see citing works

TrustPKBG: Trustworthy Protocol Knowledge Blueprints Generation via LLM for Low-Altitude AAV Networks

Wu, F., Zhang, Y., Li, G., Li, J., & Ma, J. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3658765 · Cited by 1 · see citing works

A Domain-specific Hallucination Evaluation Framework for AI Agents in Substation Operation and Maintenance Scenarios

Wang, Z., Li, L., Chen, L., Xu, X., Li, M., & Su, Y. (2025) · International Conference on Wireless Communication, Networking and Internet of Things (WCNIoT) · doi:10.1109/wcniot67424.2025.11381315 · Cited by 1 · see citing works

Exploring question answering: metric analysis and evaluation framework for enhanced interpretability

Navarro, L. C., Mucciaccia, S. S., Mutz, F. W., Paixão, T. M., Badue, C. S., Souza, A. F. D., & Oliveira-Santos, T. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12053-8 · Cited by 1 · see citing works

Optimising clinical information extraction: a comparative study of retrieval-augmented generation techniques in clinical notes

Zhang, H., Vithanage, D., Deng, C., & Yu, P. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105053 · Cited by 2 · see citing works

Unsupervised Summarization Re-ranking

Ravaut, M., Joty, S., & Chen, N. F. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.529 · Cited by 1 · see citing works

Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization

Zhu, R., Qi, J., & Lau, J. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.377 · Cited by 4 · see citing works

Multi-View Source Ablation for Faithful Summarization

Cao, S., Ma, L., Lu, D., Logan, R. L., Tetreault, J., & Jaimes, A. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.151 · Cited by 1 · see citing works

Joint Contrastive Learning for Factual Consistency Evaluation of Cross-Lingual Abstract Summarization

Guo, B., Feng, C., Liu, F., Li, X., & Wang, X. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7894-6_11 · Cited by 1 · see citing works

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 · see citing works

FaCTQA:Detecting and Localizing Factual Errors in Generated Summaries Through Question and Answering from Heterogeneous Models

Dutta, T., & Liu, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650810 · Cited by 1 · see citing works

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 · see citing works

PlainQAFact: Retrieval-augmented factual consistency evaluation metric for biomedical plain language summarization

You, Z., & Guo, Y. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105019 · Cited by 2 · see citing works

GIER: Gap-Driven Self-Refinement for Large Language Models

Dewri, R. (2025) · arXiv · doi:10.48550/arxiv.2509.00325

Attributable by Construction: Claim-Anchored Provenance for Multi-Document Summarization

Guan, S. (2026) · arXiv · doi:10.48550/arxiv.2606.23989

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

Multi Document Summarization Evaluation in the Presence of Damaging Content

Manevich, A., Carmel, D., Cohen, N., Kravi, E., & Shapira, O. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.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

Integrating Concept Associations for Query Focused Knowledge Summarization

Wang, J., Liu, Z., Sun, Y., & Li, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890022

Mitigating Multi-hop Hallucination in Large Language Models with Non-authoritative Knowledge Sources

Yuan, H., Cao, P., Jin, Z., Chen, Y., Liu, K., & Zhao, J. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1809-5_17

Methods to Evaluate and Improve the Quality of Text Summarization

Nguyen, H. (2025) · doi:10.12794/metadc2443390

T3: A Novel Zero-Shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task

Tong, X., Zhu, Y., Fan, S., & Xu, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0014-7_12

FARE: Factual Alignment for Reliable Evaluation in Text Summarization

Wang, Z., Zhou, C., Zhang, J., & Hu, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-94898-5_54

FactoScalpel: Locating and Injecting Knowledge in Transformer to Enhance Factual Consistency in Abstractive Summarization

Hao, X., Li, Y., & Li, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_10

LLM-Based Abstractive Summarization of PubMed Articles

Ho, E., Heidari, M., Zad, S., Boroojeni, K. G., & Headden, C. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-0433-6_14

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

On Improving Summarization Factual Consistency from Natural Language Feedback

Liu, Y., Deb, B., Teruel, M., Halfaker, A., Radev, D., & Awadallah, A. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.844 · Cited by 17 · see citing works

Interpretable Evaluation of Factual Consistency in Abstractive Summarization

Hegger, D. (2023) · TU/e Research Portal

Less is More for Improving Automatic Evaluation of Factual Consistency

Wang, T., Kulkarni, N., & Qi, Y. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-industry.27 · Cited by 1 · see citing works

Improving Factual Consistency of Abstractive Summarization via Question Answering

Nan, F., Nogueira dos Santos, C., Zhu, H., Ng, P., McKeown, K., Nallapati, R., Zhang, D., Wang, Z., Arnold, A. O., & Xiang, B. (2021) · 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) · doi:10.18653/v1/2021.acl-long.536 · Cited by 78 · see citing works

TRUE: Re-evaluating Factual Consistency Evaluation

Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (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.287 · Cited by 75 · see citing works