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Contrastive learning models have achieved great success in unsupervised visual representation learning, which maximize the similarities between feature representations of different views of the same image, while minimize the similarities between feature representations of views of different images.
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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Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
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ROUGE: A Package for Automatic Evaluation of Summaries
Lin, C.-Y. 2004 · 2004
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Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B.; Strub, F.; Altché, F.; Tallec, C.; Richemond, P. H.; Buchatskaya, E.; Doersch, C.; Pires, B. A.; Guo, Z. D.; Azar, M. G.; et al. 2020 · 2006
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Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints
Durrett, G.; Berg-Kirkpatrick, T.; and Klein, D. 2016 · 2008
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The new york times annotated corpus
Sandhaus, E. 2008 · 2008
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Exploring Simple Siamese Representation Learning
Chen, X.; and He, K. 2020 · 2011
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Automatic summarization
Nenkova, A.; and McKeown, K. 2011 · 2011
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Ctrlsum: Towards generic controllable text summarization
He, J.; Kryściński, W.; McCann, B.; Rajani, N.; and Xiong, C. 2020a · 2012
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Efficient Estimation of Word Representations in Vector Space
Mikolov, T.; Chen, K.; Corrado, G. S.; and Dean, J. 2013 · 2013
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Sequence to sequence learning with neural networks
Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014 · 2014
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Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D.; Cho, K.; and Bengio, Y. 2015 · 2015
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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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Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Nallapati, R.; Zhou, B.; dos Santos, C.; Gu̇lçehre, Ç.; and Xiang, B. 2016 · 2016
Cited alongside, same era.
Rethinking the Inception Architecture for Computer Vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 2016 · 2016
Cited alongside, same era.
Get To The Point: Summarization with Pointer-Generator Networks
See, A.; Liu, P. J.; and Manning, C. D. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Bottom-Up Abstractive Summarization
Gehrmann, S.; Deng, Y.; and Rush, A. 2018 · 2018
Cited alongside, same era.
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Narayan, S.; Cohen, S. B.; and Lapata, M. 2018 · 2018
A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
Later among the works it cites.
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
Iter, D.; Guu, K.; Lansing, L.; and Jurafsky, D. 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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Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Wu, H.; Ma, T.; Wu, L.; Manyumwa, T.; and Ji, S. 2020 · 2020
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Cited alongside, same era.
A Deep Reinforced Model for Abstractive Summarization
Paulus, R.; Xiong, C.; and Socher, R. 2018 · 2018
Cited alongside, same era.
Unsupervised feature learning via non-parametric instance discrimination
Wu, Z.; Xiong, Y.; Yu, S. X.; and Lin, D. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Dong, L.; Yang, N.; Wang, W.; Wei, F.; Liu, X.; Wang, Y.; Gao, J.; Zhou, M.; and Hon, H.-W. 2019 · 2019
Cited alongside, same era.
Text Summarization with Pretrained Encoders
Liu, Y.; and Lapata, M. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
Cited alongside, same era.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Zhang, J.; Zhao, Y.; Saleh, M.; and Liu, P. 2020 · 2020
Later among the works it cites.
Extractive Summarization as Text Matching
Zhong, M.; Liu, P.; Chen, Y.; Wang, D.; Qiu, X.; and Huang, X. 2020 · 2020
Later among the works it cites.
Pre-training for Abstractive Document Summarization by Reinstating Source Text
Zou, Y.; Zhang, X.; Lu, W.; Wei, F.; and Zhou, M. 2020 · 2020
Later among the works it cites.
Better Fine-Tuning by Reducing Representational Collapse
Aghajanyan, A.; Shrivastava, A.; Gupta, A.; Goyal, N.; Zettlemoyer, L.; and Gupta, S. 2021 · 2021
Closest in time.
GSum: A General Framework for Guided Neural Abstractive Summarization
Dou, Z.-Y.; Liu, P.; Hayashi, H.; Jiang, Z.; and Neubig, G. 2021 · 2021
Closest in time.
RefSum: Refactoring Neural Summarization
Liu, Y.; Dou, Z.-Y.; and Liu, P. 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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