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Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE.
Evaluating the factual consistency of abstractive text summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1910
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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English gigaword
David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda. 2003 · 2003
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Boosting factual correctness of abstractive summarization with knowledge graph
Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang. 2020 · 2003
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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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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A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
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Oriol Vinyals and Quoc Le. 2015 · 2015
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Abstractive sentence summarization with attentive recurrent neural networks
Sumit Chopra, Michael Auli, and Alexander M Rush. 2016 · 2016
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016 · 2016
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spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
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Six challenges for neural machine translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 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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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018 · 2018
Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo FR Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J Liu, and Mohammad Saleh. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
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LeafNATS: An open-source toolkit and live demo system for neural abstractive text summarization
Tian Shi, Ping Wang, and Chandan K. Reddy. 2019 · 2019
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Feqa: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
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Hallucinations in neural machine translation
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018 · 2018
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Ensure the correctness of the summary: Incorporate entailment knowledge into abstractive sentence summarization
Haoran Li, Junnan Zhu, Jiajun Zhang, and Chengqing Zong. 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. 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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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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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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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 2020
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Factual error correction for abstractive summarization models
Cao Meng, Yue Cheung Dong, Jiapeng Wu, and Jackie Chi Kit. 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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Automatic fact-guided sentence modification
Darsh J Shah, Tal Schuster, and Regina Barzilay. 2020 · 2020
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Joint parsing and generation for abstractive summarization
Kaiqiang Song, Logan Lebanoff, Qipeng Guo, Xipeng Qiu, Xiangyang Xue, Chen Li, Dong Yu, and Fei Liu. 2020 · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu. 2020a · 2020
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