Fetching the paper…
Reading the bibliography…
As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary.
Neural extractive text summarization with syntactic compression
Jiacheng Xu and Greg Durrett. 2019 · 1902
Earlier work this paper cites.
Pretraining-based natural language generation for text summarization
Haoyu Zhang, Yeyun Gong, Yu Yan, Nan Duan, Jianjun Xu, Ji Wang, Ming Gong, and Ming Zhou. 2019a · 1902
Earlier work this paper cites.
Fine-tune bert for extractive summarization
Yang Liu. 2019 · 1903
Earlier work this paper cites.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019b · 1904
Earlier work this paper cites.
Efficient adaptation of pretrained transformers for abstractive summarization
Andrew Hoang, Antoine Bosselut, Asli Celikyilmaz, and Yejin Choi. 2019 · 1906
Earlier work this paper cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 1906
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016 · 1937
Earlier work this paper cites.
Introduction to reinforcement learning , volume 2
Richard S Sutton, Andrew G Barto, et al. 1998 · 1998
Earlier work this paper cites.
Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart J. Russell. 1999 · 1999
Earlier work this paper cites.
Automatic summarization
Inderjeet Mani and Mark T Maybury. 2001 · 2001
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Learning-based single-document summarization with compression and anaphoricity constraints
Greg Durrett, Taylor Berg-Kirkpatrick, and Dan Klein. 2016 · 2008
Earlier work this paper cites.
The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings
Yoshua Bengio and Yann LeCun, editors. 2015 · 2015
Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Deep communicating agents for abstractive summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018 · 2018
Later among the works it cites.
Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
Later among the works it cites.
Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
Later among the works it cites.
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 · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
Cited alongside, same era.
Abstractive sentence summarization with attentive recurrent neural networks
Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016 · 2016
Cited alongside, same era.
Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016 · 2016
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gulçehre, and Bing Xiang. 2016 · 2016
Cited alongside, same era.
Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016 · 2016
Cited alongside, same era.
Efficient summarization with read-again and copy mechanism
Wenyuan Zeng, Wenjie Luo, Sanja Fidler, and Raquel Urtasun. 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Guiding generation for abstractive text summarization based on key information guide network
Chenliang Li, Weiran Xu, Si Li, and Sheng Gao. 2018 · 2018
Later among the works it cites.
Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Later among the works it cites.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
Later among the works it cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Closest in time.
Neural sequence-to-sequence models from weak feedback with bipolar ramp loss
Laura Jehl, Carolin Lawrence, and Stefan Riezler. 2019 · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Closest in time.
Deepchannel: Salience estimation by contrastive learning for extractive document summarization
Jiaxin Shi, Chen Liang, Lei Hou, Juanzi Li, Zhiyuan Liu, and Hanwang Zhang. 2019 · 2019
Closest in time.
BERT has a mouth, and it must speak: BERT as a Markov random field language model
Alex Wang and Kyunghyun Cho. 2019 · 2019
Closest in time.