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With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding.
Rouge: A package for automatic evaluation of summaries
C. Lin. 2004 · 2004
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Event-centric summary generation
Lucy Vanderwende, Michele Banko, and Arul Menezes. 2004 · 2004
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A study of global inference algorithms in multi-document summarization
Ryan McDonald. 2007 · 2007
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Overview of the tac 2008 update summarization task
Hoa Trang Dang and Karolina Owczarzak. 2008 · 2008
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A scalable global model for summarization
Dan Gillick and Benoit Favre. 2009 · 2009
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Summarization with a joint model for sentence extraction and compression
Andre F. T. Martins and Noah A. Smith. 2009 · 2009
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Fast and robust compressive summarization with dual de-composition and multi-task learning
Miguel B. Almeida and Andre F. T. Martins. 2013 · 2013
Earlier work this paper cites.
Abstract meaning representation for sembanking
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Martha Palmer, Philipp Koehn, and Nathan Schneider. 2013 · 2013
Earlier work this paper cites.
A discriminative graph-based parser for the abstract meaning representation
Jeffrey Flanigan, Sam Thomson, Jaime Carbonell, Chris Dyer, and Noah A. Smith. 2014 · 2014
Earlier work this paper cites.
Abstractive summarization of product reviews using discourse structure
Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond T. Ng, and Bita Nejat. 2014 · 2014
Earlier work this paper cites.
Deft phase 2 amr annotation r1 ldc2015e86. philadelphia: Linguistic data consortium
Kevin Knight, Laura Baranescu, Claire Bonial, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Daniel Marcu, Martha Palmer, and Nathan Schneider. 2014 · 2014
Cited alongside, same era.
Unsupervised sentence enhancement for automatic summarization
Gerald Penn and Jackie Chi Kit Cheung. 2014 · 2014
Cited alongside, same era.
Amr-guidelines
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 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.
Toward abstractive summarization using semantic representations
Fei Liu, Flanigan Jeffrey, Thomson Sam, Sadeh Norman, and Smith Noah A. 2015 · 2015
Cited alongside, same era.
Neural headline generation on abstract meaning representation
Sho Takase, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, and Masaaki Nagata. 2016 · 2016
Later among the works it cites.
Camr at semeval-2016 task 8: An extended transition-based amr parser
Chuan Wang, Sameer Pradhan, Xiaoman Pan, Heng Ji, and Nianwen Xue. 2016 · 2016
Later among the works it cites.
Amr parsing with an incremental joint model
Junsheng Zhou, Feiyu Xu, Hans Uszkoreit, Weiguang QU, Ran Li, and Yanhui Gu. 2016 · 2016
Later among the works it cites.
Neural amr: Sequence-to-sequence models for parsing and generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 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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Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Cited alongside, same era.
Amrica: an amr inspector for cross-language alignments
Naomi Saphra and Adam Lopez. 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.
Generation from abstract meaning representation using tree transducers
Jeffrey Flanigan, Jaime Carbonell, Chris Dyer, and Noah A. Smith. 2016 · 2016
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Ça ̆glar G ulçehre, and Bing Xiang. 2016 · 2016
Cited alongside, same era.
Closest in time.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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Addressing the data sparsity issue in neural amr parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Closest in time.
Amr-to-text generation as a traveling salesman problem
Linfeng Song, Yue Zhang, Xiaochang Peng, Zhiguo Wang, and Daniel Gildea. 2016 · 2089
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