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Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods.
Evaluating the factual consistency of abstractive text summarization
Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1910
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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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Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Yuhao Zhang, Derek Merck, Emily Bao Tsai, Christopher D Manning, and Curtis P Langlotz. 2019b · 1911
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu. 2019a · 1912
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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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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2004
Cited alongside, same era.
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
Cited alongside, same era.
Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
Cited alongside, same era.
Soft layer-specific multi-task summarization with entailment and question generation
Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2018 · 2018
Cited alongside, same era.
Ensure the correctness of the summary: Incorporate entailment knowledge into abstractive sentence summarization
Haoran Li, Junnan Zhu, Jiajun Zhang, and Chengqing Zong. 2018 · 2018
Cited alongside, same era.
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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Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples
Krtin Kumar and Jackie Chi Kit Cheung. 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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Multi-fact correction in abstractive text summarization
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Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019 · 2019
Cited alongside, same era.
Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung, and Jingjing Liu. 2020 · 2020
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