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Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization.
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
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Better summarization evaluation with word embeddings for ROUGE
Jun-Ping Ng and Viktoria Abrecht. 2015 · 1930
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Regression analysis
Evan J. Williams. 1959 · 1959
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Thirteen ways to look at the correlation coefficient
W Alan Lee Rodgers. 1988 · 1988
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Learning by semantic similarity makes abstractive summarization better
Wonjin Yoon, Yoon Sun Yeo, Minbyul Jeong, Bong-Jun Yi, and Jaewoo Kang. 2020 · 2002
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Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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ORANGE: a method for evaluating automatic evaluation metrics for machine translation
Chin-Yew Lin and Franz Josef Och. 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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Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P Parikh. 2020 · 2004
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Heterogeneous graph neural networks for extractive document summarization
Danqing Wang, Pengfei Liu, Yining Zheng, Xipeng Qiu, and Xuanjing Huang. 2020 · 2004
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Extractive summarization as text matching
Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2020 · 2004
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Overview of duc 2005
Hoa Trang Dang. 2005 · 2005
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A probabilistic interpretation of precision, recall and f-score, with implication for evaluation
Cyril Goutte and Eric Gaussier. 2005 · 2005
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Overview of duc 2006
Hoa Trang Dang. 2006 · 2006
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An information-theoretic approach to automatic evaluation of summaries
Chin-Yew Lin, Guihong Cao, Jianfeng Gao, and Jian-Yun Nie. 2006 · 2006
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Overview of the tac 2008 update summarization task
Hoa Dang and Karolina Owczarzak. 2008 · 2008
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Overview of the tac 2009 summarization track
Hoa Dang and Karolina Owczarzak. 2009 · 2009
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Computing krippendorff’s alpha-reliability
Klaus Krippendorff. 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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A decade of automatic content evaluation of news summaries: Reassessing the state of the art
Peter A. Rankel, John M. Conroy, Hoa Trang Dang, and Ani Nenkova. 2013 · 2013
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Testing for significance of increased correlation with human judgment
Yvette Graham and Timothy Baldwin. 2014 · 2014
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The Stanford CoreNLP natural language processing toolkit
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
Controllable abstractive summarization
Angela Fan, David Grangier, and Michael Auli. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
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Content selection in deep learning models of summarization
Chris Kedzie, Kathleen McKeown, and Hal Daume III. 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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Neural latent extractive document summarization
Xingxing Zhang, Mirella Lapata, Furu Wei, and Ming Zhou. 2018 · 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 · 2018
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Re-evaluating automatic summarization with BLEU and 192 shades of ROUGE
Yvette Graham. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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From word embeddings to document distances
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. 2015 · 2015
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Results of the WMT16 metrics shared task
Ondřej Bojar, Yvette Graham, Amir Kamran, and Miloš Stanojević. 2016 · 2016
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Ça glar Gulçehre, and Bing Xiang. 2016 · 2016
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Why we need new evaluation metrics for NLG
Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, and Verena Rieser. 2017 · 2017
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Better rewards yield better summaries: Learning to summarise without references
Florian Böhm, Yang Gao, Christian M. Meyer, Ori Shapira, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Sentence mover’s similarity: Automatic evaluation for multi-sentence texts
Elizabeth Clark, Asli Celikyilmaz, and Noah A Smith. 2019 · 2019
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Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019a · 2019
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Studying summarization evaluation metrics in the appropriate scoring range
Maxime Peyrard. 2019 · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019 · 2019
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Crowdsourcing lightweight pyramids for manual summary evaluation
Ori Shapira, David Gabay, Yang Gao, Hadar Ronen, Ramakanth Pasunuru, Mohit Bansal, Yael Amsterdamer, and Ido Dagan. 2019 · 2019
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MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance
Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, and Steffen Eger. 2019 · 2019
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Searching for effective neural extractive summarization: What works and what’s next
Ming Zhong, Pengfei Liu, Danqing Wang, Xipeng Qiu, and Xuan-Jing Huang. 2019 · 2019
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake Vand erPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1. 0 Contributors. 2020 · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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