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In this paper, we propose a novel pretraining-based encoder-decoder framework, which can generate the output sequence based on the input sequence in a two-stage manner.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Learning-based single-document summarization with compression and anaphoricity constraints
Greg Durrett, Taylor Berg-Kirkpatrick, and Dan Klein · 2016
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Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O. K. Li · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2017
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Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou · 2017
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Learning to generate reviews and discovering sentiment
Alec Radford, Rafal Józefowicz, and Ilya Sutskever · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning · 2017
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Deep Communicating Agents for Abstractive Summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi · 2018
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Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush · 2018
Ranking Sentences for Extractive Summarization with Reinforcement Learning
Shashi Narayan, Shay B Cohen, and Mirella Lapata · 2018
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, Richard Socher, and Palo Alto · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
Jiaxin Shi, Chen Liang, Lei Hou, Juanzi Li, Zhiyuan Liu, and Hanwang Zhang · 2018
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A unified model for extractive and abstractive summarization using inconsistency loss
Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, and Min Sun · 2018
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Improving abstraction in text summarization
Wojciech Kryściński, Romain Paulus, Caiming Xiong, and Richard Socher · 2018
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Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling
Wei Li, Xinyan Xiao, Yajuan Lyu, and Yuanzhuo Wang · 2018
Cited alongside, same era.
Neural latent extractive document summarization
Xingxing Zhang, Mirella Lapata, Furu Wei, and Ming Zhou · 2018
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Neural document summarization by jointly learning to score and select sentences
Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, and Tiejun Zhao · 2018
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Language Model Pre-training for Hierarchical Document Representations
Ming-Wei Chang, Kristina Toutanova, Kenton Lee, and Jacob Devlin · 2019
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