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Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.
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. 2019 · 1902
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English gigaword, linguistic data consortium
David Graff and C Cieri. 2003 · 2003
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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The new york times annotated corpus
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 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
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Çağlar Gülçehre, Bing Xiang, et al. 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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Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 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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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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A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Soft layer-specific multi-task summarization with entailment and question generation
Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2018 · 2018
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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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Closed-book training to improve summarization encoder memory
Yichen Jiang and Mohit Bansal. 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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Ensure the correctness of the summary: Incorporate entailment knowledge into abstractive sentence summarization
Haoran Li, Junnan Zhu, Jiajun Zhang, and Chengqing Zong. 2018a · 2018
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Generating wikipedia by summarizing long sequences
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Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz 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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Fast abstractive summarization with reinforce-selected sentence rewriting
Yen-Chun Chen and Mohit Bansal. 2018 · 2018
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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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Xnli: Evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov. 2018 · 2018
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 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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Multi-reward reinforced summarization with saliency and entailment
Ramakanth Pasunuru and Mohit Bansal. 2018 · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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FEVER: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Learning to extract coherent summary via deep reinforcement learning
Yuxiang Wu and Baotian Hu. 2018 · 2018
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On the abstractiveness of neural document summarization
Fangfang Zhang, Jin-ge Yao, and Rui Yan. 2018 · 2018
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo F. R. 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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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 2019
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