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Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets.
Summarization evaluation methods: Experiments and analysis
Hongyan Jing, Regina Barzilay, Kathleen McKeown, and Michael Elhadad. 1998 · 1998
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English gigaword
David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda. 2003 · 2003
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Overview of duc 2005
Hoa Trang Dang. 2005 · 2005
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Automated summarization evaluation with basic elements
Eduard Hovy, Chin-Yew Lin, Liang Zhou, and Junichi Fukumoto. 2006 · 2006
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Task-based evaluation of text summarization using relevance prediction
Stacy President Hobson, Bonnie J Dorr, Christof Monz, and Richard Schwartz. 2007 · 2007
Earlier work this paper cites.
The pyramid method: Incorporating human content selection variation in summarization evaluation
Ani Nenkova, Rebecca Passonneau, and Kathleen McKeown. 2007 · 2007
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The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
Earlier work this paper cites.
Depeval(summ): Dependency-based evaluation for automatic summaries
Karolina Owczarzak. 2009 · 2009
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On automated evaluation of readability of summaries: Capturing grammaticality, focus, structure and coherence
Ravikiran Vadlapudi and Rahul Katragadda. 2010 · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer. 2011 · 2011
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Overview of the tac 2011 summarization track: Guided task and aesop task
Karolina Owczarzak and Hoa Trang Dang. 2011 · 2011
Earlier work this paper cites.
Peak: Pyramid evaluation via automated knowledge extraction
Qian Yang, Rebecca J. Passonneau, and Gerard de Melo. 2016 · 2011
Cited alongside, same era.
Evaluation measures for text summarization
Josef Steinberger and Karel Ježek. 2012 · 2012
Cited alongside, same era.
Automatically assessing machine summary content without a gold standard
Annie Louis and Ani Nenkova. 2013 · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
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OpenNMT: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017 · 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 · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
An improved non-monotonic transition system for dependency parsing
Matthew Honnibal and Mark Johnson. 2015 · 2015
Cited alongside, same era.
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.
A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D Manning. 2016 · 2016
Cited alongside, same era.
Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Skipflow: Incorporating neural coherence features for end-to-end automatic text scoring
Yi Tay, Minh C Phan, Luu Anh Tuan, and Siu Cheung Hui. 2017 · 2017
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Deep communicating agents for abstractive summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018 · 2018
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Automatic pyramid evaluation exploiting edu-based extractive reference summaries
Tsutomu Hirao, Hidetaka Kamigaito, and Masaaki Nagata. 2018 · 2018
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Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018
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