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Generating factual-consistent summaries is a challenging task for abstractive summarization.
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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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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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. 2004 · 2004
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y. 2004 · 2004
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Contrastive learning with adversarial perturbations for conditional text generation
Lee, S.; Lee, D. B.; and Hwang, S. J. 2020 · 2012
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Contrastive learning with adversarial perturbations for conditional text generation
Lee, S.; Lee, D. B.; and Hwang, S. J. 2020 · 2012
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Leveraging linguistic structure for open domain information extraction
Angeli, G.; Premkumar, M. J. J.; and Manning, C. D. 2015 · 2015
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Leveraging linguistic structure for open domain information extraction
Angeli, G.; Premkumar, M. J. J.; and Manning, C. D. 2015 · 2015
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Convolutional sequence to sequence learning
Gehring, J.; Auli, M.; Grangier, D.; Yarats, D.; and Dauphin, Y. N. 2017 · 2017
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Billion-scale similarity search with GPUs
Johnson, J.; Douze, M.; and Jégou, H. 2017 · 2017
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Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Nallapati, R.; Zhai, F.; and Zhou, B. 2017 · 2017
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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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Convolutional sequence to sequence learning
Gehring, J.; Auli, M.; Grangier, D.; Yarats, D.; and Dauphin, Y. N. 2017 · 2017
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Billion-scale similarity search with GPUs
Johnson, J.; Douze, M.; and Jégou, H. 2017 · 2017
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Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Nallapati, R.; Zhai, F.; and Zhou, B. 2017 · 2017
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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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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
Li, H.; Zhu, J.; Zhang, J.; and Zong, C. 2018 · 2018
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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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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
Li, H.; Zhu, J.; Zhang, J.; and Zong, C. 2018 · 2018
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FLAIR: An easy-to-use framework for state-of-the-art NLP
Akbik, A.; Bergmann, T.; Blythe, D.; Rasul, K.; Schweter, S.; and Vollgraf, R. 2019 · 2019
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Assessing the factual accuracy of generated text
Goodrich, B.; Rao, V.; Liu, P. J.; and Saleh, M. 2019 · 2019
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
Cited alongside, same era.
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle
West, P.; Holtzman, A.; Buys, J.; and Choi, Y. 2019 · 2019
Cited alongside, same era.
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach
Yang, Z.; Cheng, Y.; Liu, Y.; and Sun, M. 2019 · 2019
Cited alongside, same era.
FLAIR: An easy-to-use framework for state-of-the-art NLP
Multi-Fact Correction in Abstractive Text Summarization
Dong, Y.; Wang, S.; Gan, Z.; Cheng, Y.; Cheung, J. C. K.; and Liu, J. 2020 · 2020
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Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward
Huang, L.; Wu, L.; and Wang, L. 2020 · 2020
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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.; and Zettlemoyer, L. 2020 · 2020
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
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Asking and Answering Questions to Evaluate the Factual Consistency of Summaries
Wang, A.; Cho, K.; and Lewis, M. 2020 · 2020
Later among the works it cites.
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Akbik, A.; Bergmann, T.; Blythe, D.; Rasul, K.; Schweter, S.; and Vollgraf, R. 2019 · 2019
Cited alongside, same era.
Assessing the factual accuracy of generated text
Goodrich, B.; Rao, V.; Liu, P. J.; and Saleh, M. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
Cited alongside, same era.
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle
West, P.; Holtzman, A.; Buys, J.; and Choi, Y. 2019 · 2019
Cited alongside, same era.
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach
Yang, Z.; Cheng, Y.; Liu, Y.; and Sun, M. 2019 · 2019
Cited alongside, same era.
Factual Error Correction for Abstractive Summarization Models
Cao, M.; Dong, Y.; Wu, J.; and Cheung, J. C. K. 2020 · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
Cited alongside, same era.
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Yamada, I.; Asai, A.; Shindo, H.; Takeda, H.; and Matsumoto, Y. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Zhang, J.; Zhao, Y.; Saleh, M.; and Liu, P. 2020 · 2020
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Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
Chen, S.; Zhang, F.; Sone, K.; and Roth, D. 2021 · 2021
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FactSumm: Factual Consistency Scorer for Abstractive Summarization
Heo, H. 2021 · 2021
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The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Huang, Y.; Feng, X.; Feng, X.; and Qin, B. 2021 · 2021
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SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
Liu, Y.; and Liu, P. 2021 · 2021
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Contrastive learning for many-to-many multilingual neural machine translation
Pan, X.; Wang, M.; Wu, L.; and Li, L. 2021 · 2021
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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
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Enhancing Factual Consistency of Abstractive Summarization
Zhu, C.; Hinthorn, W.; Xu, R.; Zeng, Q.; Zeng, M.; Huang, X.; and Jiang, M. 2021 · 2021
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Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection
Chen, S.; Zhang, F.; Sone, K.; and Roth, D. 2021 · 2021
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FactSumm: Factual Consistency Scorer for Abstractive Summarization
Heo, H. 2021 · 2021
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The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Huang, Y.; Feng, X.; Feng, X.; and Qin, B. 2021 · 2021
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SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization
Liu, Y.; and Liu, P. 2021 · 2021
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Contrastive learning for many-to-many multilingual neural machine translation
Pan, X.; Wang, M.; Wu, L.; and Li, L. 2021 · 2021
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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
Closest in time.
Enhancing Factual Consistency of Abstractive Summarization
Zhu, C.; Hinthorn, W.; Xu, R.; Zeng, Q.; Zeng, M.; Huang, X.; and Jiang, M. 2021 · 2021
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