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Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization.
Automatic condensation of electronic publications by sentence selection
Ronald Brandow, Karl Mitze, and Lisa F Rau. 1995 · 1995
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Manual and automatic evaluation of summaries
Chin-Yew Lin and Eduard Hovy. 2002 · 2002
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Pre-training a language model without human language
Cheng-Han Chiang and Hung-yi Lee. 2020 · 2012
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Domain-independent abstract generation for focused meeting summarization
Lu Wang and Claire Cardie. 2013 · 2013
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Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
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Neural network-based abstract generation for opinions and arguments
Lu Wang and Wang Ling. 2016 · 2016
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The e2e dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser. 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
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Challenges in data-to-document generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2017 · 2017
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A discourse-aware attention model for abstractive summarization of long documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
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Generating wikipedia by summarizing long sequences
Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018 · 2018
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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A graph-to-sequence model for amr-to-text generation
Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018 · 2018
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Neural wikipedian: Generating textual summaries from knowledge base triples
Pavlos Vougiouklis, Hady Elsahar, Lucie-Aimée Kaffee, Christophe Gravier, Frédérique Laforest, Jonathon Hare, and Elena Simperl. 2018 · 2018
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Identifying and reducing gender bias in word-level language models
Shikha Bordia and Samuel Bowman. 2019 · 2019
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Meansum: a neural model for unsupervised multi-document abstractive summarization
Eric Chu and Peter Liu. 2019 · 2019
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Racial bias in hate speech and abusive language detection datasets
Thomas Davidson, Debasmita Bhattacharya, and Ingmar Weber. 2019 · 2019
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Modeling content and structure for abstractive review summarization
Shima Gerani, Giuseppe Carenini, and Raymond T Ng. 2019 · 2019
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Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Scisummnet: A large annotated corpus and content-impact models for scientific paper summarization with citation networks
Michihiro Yasunaga, Jungo Kasai, Rui Zhang, Alexander R Fabbri, Irene Li, Dan Friedman, and Dragomir R Radev. 2019 · 2019
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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What do neural networks learn when trained with random labels?
Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin, Robert JN Baldock, Olivier Bousquet, Sylvain Gelly, and Daniel Keysers. 2020 · 2020
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Emergent linguistic structure in artificial neural networks trained by self-supervision
Christopher D Manning, Kevin Clark, John Hewitt, Urvashi Khandelwal, and Omer Levy. 2020 · 2020
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Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
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Countering the effects of lead bias in news summarization via multi-stage training and auxiliary losses
Matt Grenander, Yue Dong, Jackie Chi Kit Cheung, and Annie Louis. 2019 · 2019
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Text generation from knowledge graphs with graph transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019 · 2019
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Open sesame: Getting inside bert’s linguistic knowledge
Yongjie Lina, Yi Chern Tana, and Robert Frankb. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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Step-by-step: Separating planning from realization in neural data-to-text generation
Amit Moryossef, Yoav Goldberg, and Ido Dagan. 2019 · 2019
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Data-to-text generation with content selection and planning
Ratish Puduppully, Li Dong, and Mirella Lapata. 2019 · 2019
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What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang. 2020 · 2020
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Learning music helps you read: Using transfer to study linguistic structure in language models
Isabel Papadimitriou and Dan Jurafsky. 2020 · 2020
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Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training
Weizhen Qi, Yu Yan, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020 · 2020
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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. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
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Pre-training for abstractive document summarization by reinstating source text
Yanyan Zou, Xingxing Zhang, Wei Lu, Furu Wei, and Ming Zhou. 2020 · 2020
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Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, and Douwe Kiela. 2021 · 2021
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Summarizing source code using a neural attention model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2083
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A convolutional attention network for extreme summarization of source code
Miltiadis Allamanis, Hao Peng, and Charles Sutton. 2016 · 2091
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