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Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text.
Advances in automatic text summarization
Inderjeet Mani and T. Mark Maybury · 1999
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The challenges of automatic summarization
U. Hahn and I. Mani · 2000
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Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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David Zajic, Bonnie J Dorr, and R. Schwartz · 2004
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Reward augmented maximum likelihood for neural structured prediction
Mohammad Norouzi, Samy Bengio, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans, et al · 2016
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning · 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
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Faithful to the original: Fact aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li · 2018
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Angela Fan, Mike Lewis, and Yann Dauphin · 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
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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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
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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Mohammad Saleh, and Peter J. Liu · 2019
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Constrained abstractive summarization: Preserving factual consistency with constrained generation, 2020
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Improving truthfulness of headline generation
Kazuki Matsumaru, Sho Takase, and Naoaki Okazaki · 2020
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald · 2020
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Looking beyond sentence-level natural language inference for downstream tasks
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, and Kartik Talamadupula · 2020
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Mind the facts: Knowledge-boosted coherent abstractive text summarization
Beliz Gunel, Chenguang Zhu, Michael Zeng, and Xuedong Huang · 2019
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Abstractive summarization: An overview of the state of the art
Som Gupta · 2019
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Multi-fact correction in abstractive text summarization
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FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab · 2020
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Go figure! a meta evaluation of factuality in summarization, 2020
Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, and Jianfeng Gao · 2020
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Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward
Luyang Huang, Lingfei Wu, and Lu Wang · 2020
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On the faithfulness for E-commerce product summarization
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Bertscore: Evaluating text generation with bert
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Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Yuhao Zhang, Derek Merck, Emily Tsai, Christopher D. Manning, and Curtis Langlotz · 2020
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Reducing quantity hallucinations in abstractive summarization
Zheng Zhao, Shay B. Cohen, and Bonnie Webber · 2020
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Detecting hallucinated content in conditional neural sequence generation
Chunting Zhou, Jiatao Gu, Mona Diab, Paco Guzman, Luke Zettlemoyer, and Marjan Ghazvininejad · 2020
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Boosting factual correctness of abstractive summarization with knowledge graph, 2020
Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang · 2020
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Nutri-bullets: Summarizing health studies by composing segments, 2021
Darsh J Shah, Lili Yu, Tao Lei, and Regina Barzilay · 2021
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