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There has been substantial progress in summarization research enabled by the availability of novel, often large-scale, datasets and recent advances on neural network-based approaches.
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Evaluating Content Selection in Summarization: The Pyramid Method
Ani Nenkova and Rebecca Passonneau. 2004 · 2004
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Evaluating information content by factoid analysis: Human annotation and stability
Simone Teufel and Hans Van Halteren. 2004 · 2004
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Overview of DUC 2005
Hoa Trang Dang. 2005 · 2005
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Automatic Text Summarization of Newswire: Lessons Learned from the Document Understanding Conference
Ani Nenkova. 2005 · 2005
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The Cambridge Dictionary of Statistics
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The New York Times Annotated Corpus
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Discourse constraints for document compression
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A Grain of Salt for the WMT Manual Evaluation
Ondřej Bojar, Miloš Ercegovčević, Martin Popel, and Omar Zaidan. 2011 · 2011
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Automatically Assessing Machine Summary Content Without a Gold Standard
Annie Louis and Ani Nenkova. 2013 · 2013
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Fact Checking: Task definition and dataset construction
Andreas Vlachos and Sebastian Riedel. 2014 · 2014
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Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Best-worst scaling: Theory, methods and applications
Jordan J Louviere, Terry N Flynn, Anthony Alfred Fred, and John Marley. 2015 · 2015
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Findings of the 2016 Conference on Machine Translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurelie Neveol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. 2016 · 2016
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Reference bias in monolingual machine translation evaluation
Marina Fomicheva and Lucia Specia. 2016 · 2016
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Findings of the 2017 Conference on Machine Translation (WMT17)
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shujian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Raphael Rubino, Lucia Specia, and Marco Turchi. 2017 · 2017
Cited alongside, same era.
Why We Need New Evaluation Metrics for {NLG}
Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, and Verena Rieser. 2017 · 2017
Cited alongside, same era.
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
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Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks
Aishwarya Jadhav and Vaibhav Rajan. 2018 · 2018
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Content Selection in Deep Learning Models of Summarization
Chris Kedzie, Kathleen McKeown, and Hal Daumé III. 2018 · 2018
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Generating Topic-Oriented Summaries Using Neural Attention
Kundan Krishna and Balaji Vasan Srinivasan. 2018 · 2018
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Improving Abstraction in Text Summarization
Wojciech Kryściński, Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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The limits of automatic summarisation according to ROUGE
Natalie Schluter. 2017 · 2017
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Cited alongside, same era.
Abstractive Document Summarization with a Graph-Based Attentional Neural Model
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2017 · 2017
Cited alongside, same era.
Detecting (Un)Important Content for Single-Document News Summarization
Yinfei Yang, Forrest Sheng Bao, and Ani Nenkova. 2017 · 2017
Cited alongside, same era.
Entity Commonsense Representation for Neural Abstractive Summarization
Reinald Kim Amplayo, Seonjae Lim, and Seung-Won Hwang. 2018 · 2018
Cited alongside, same era.
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2018 · 2018
Cited alongside, same era.
Deep communicating agents for abstractive summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018 · 2018
Cited alongside, same era.
Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network
Chenliang Li, Weiran Xu, Si Li, and Sheng Gao. 2018a · 2018
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Abstract Meaning Representation for Multi-Document Summarization
Kexin Liao, Logan Lebanoff, and Fei Liu. 2018 · 2018
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Global Encoding for Abstractive Summarization
Junyang Lin, Shuming Ma, and Qi Su. 2018 · 2018
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Don’t Give Me the Details, Just the Summary! Topic-aware Convolutional Neural Networks for Extreme Summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018b · 2018
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Ranking Sentences for Extractive Summarization with Reinforcement Learning
Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018c · 2018
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Multi-Reward Reinforced Summarization with Saliency and Entailment
Ramakanth Pasunuru and Mohit Bansal. 2018 · 2018
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Objective Function Learning to Match Human Judgements for Optimization-Based Summarization
Maxime Peyrard and Iryna Gurevych. 2018 · 2018
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Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function
Shinsaku Sakaue, Tsutomu Hirao, Masaaki Nishino, and Masaaki Nagata. 2018 · 2018
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Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings
Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, and Fang Chen. 2018 · 2018
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Structure-Infused Copy Mechanisms for Abstractive Summarization
Kaiqiang Song, Lin Zhao, and Fei Liu. 2018 · 2018
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Jointly extracting and compressing documents with summary state representations
Afonso Mendes, Shashi Narayan, Sebastião Miranda, Zita Marinho, André F. T. Martins, and Shay B. Cohen. 2019 · 2019
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Document Modeling with External Attention for Sentence Extraction
Shashi Narayan, Ronald Cardenas, Nikos Papasarantopoulos, Shay B Cohen, Mirella Lapata, Jiangsheng Yu, and Yi Chang. 2018a · 2030
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