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The issue of factual consistency in abstractive summarization has received extensive attention in recent years, and the evaluation of factual consistency between summary and document has become an important and urgent task.
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., and Stoyanov, V. (2019) · 1907
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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) · 1910
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On faithfulness and factuality in abstractive summarization
Maynez, J., Narayan, S., Bohnet, B., and McDonald, R. (2020) · 1919
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“cloze procedure”: A new tool for measuring readability
Taylor, W. L. (1953) · 1953
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Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002) · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. (2004) · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Banerjee, S., and Lavie, A. (2005) · 2005
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Adversarial nli for factual correctness in text summarisation models
Barrantes, M., Herudek, B., and Wang, R. (2020) · 2005
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Adam: A method for stochastic optimization
Kingma, D. P., and Ba, J. (2014) · 2014
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Teaching machines to read and comprehend
Hermann, K. M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P. (2015) · 2015
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
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Faithful to the original: Fact aware neural abstractive summarization
Cao, Z., Wei, F., Li, W., and Li, S. (2018) · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Narayan, S., Cohen, S., and Lapata, M. (2018) · 2018
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Falke, T., Ribeiro, L. F., Utama, P. A., Dagan, I., and Gurevych, I. (2019) · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C., and Toutanova, L. K. (2019) · 2019
Cited alongside, same era.
Neural text summarization: A critical evaluation
Kryściński, W., Keskar, N. S., McCann, B., Xiong, C., and Socher, R. (2019) · 2019
Cited alongside, same era.
Answers unite! unsupervised metrics for reinforced summarization models
Scialom, T., Lamprier, S., Piwowarski, B., and Staiano, J. (2019) · 2019
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., and Artzi, Y. (2019) · 2019
Cited alongside, same era.
Factual error correction for abstractive summarization models
Asking and answering questions to evaluate the factual consistency of summaries
Wang, A., Cho, K., and Lewis, M. (2020) · 2020
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Understanding the extent to which content quality metrics measure the information quality of summaries
Deutsch, D., and Roth, D. (2021) · 2021
Later among the works it cites.
Go figure: A meta evaluation of factuality in summarization
Gabriel, S., Celikyilmaz, A., Jha, R., Choi, Y., and Gao, J. (2021) · 2021
Later among the works it cites.
Annotating and modeling fine-grained factuality in summarization
Goyal, T., and Durrett, G. (2021) · 2021
Later among the works it cites.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G. (2021) · 2021
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Cao, M., Dong, Y., Wu, J., and Cheung, J. C. K. (2020) · 2020
Cited alongside, same era.
Feqa: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Durmus, E., He, H., and Diab, M. (2020) · 2020
Cited alongside, same era.
Evaluating the factual consistency of abstractive text summarization
Kryściński, W., McCann, B., Xiong, C., and Socher, R. (2020) · 2020
Cited alongside, same era.
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., and Zettlemoyer, L. (2020) · 2020
Cited alongside, same era.
Comet: A neural framework for mt evaluation
Rei, R., Stewart, C., Farinha, A. C., and Lavie, A. (2020) · 2020
Cited alongside, same era.
Bleurt: Learning robust metrics for text generation
Sellam, T., Das, D., and Parikh, A. (2020) · 2020
Cited alongside, same era.
Fill in the blanc: Human-free quality estimation of document summaries
Vasilyev, O., Dharnidharka, V., and Bohannon, J. (2020) · 2020
Cited alongside, same era.
Nan, F., Santos, C. N. d., Zhu, H., Ng, P., McKeown, K., Nallapati, R., Zhang, D., Wang, Z., Arnold, A. O., and Xiang, B. (2021) · 2021
Later among the works it cites.
Understanding factuality in abstractive summarization with frank: A benchmark for factuality metrics
Pagnoni, A., Balachandran, V., and Tsvetkov, Y. (2021) · 2021
Later among the works it cites.
Questeval: Summarization asks for fact-based evaluation
Scialom, T., Dray, P.-A., Gallinari, P., Lamprier, S., Piwowarski, B., Staiano, J., and Wang, A. (2021) · 2021
Later among the works it cites.
Glm: General language model pretraining with autoregressive blank infilling
Du, Z., Qian, Y., Liu, X., Ding, M., Qiu, J., Yang, Z., and Tang, J. (2022) · 2022
Closest in time.
Ffci: A framework for interpretable automatic evaluation of summarization
Koto, F., Baldwin, T., and Lau, J. H. (2022) · 2022
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Summac: Re-visiting nli-based models for inconsistency detection in summarization
Laban, P., Schnabel, T., Bennett, P. N., and Hearst, M. A. (2022) · 2022
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Evaluating machine common sense via cloze testing
Qasemi, E., Kezar, L., Pujara, J., and Szekely, P. (2022) · 2022
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