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Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues.
Bertscore: Evaluating text generation with bert
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Rouge: A package for automatic evaluation of summaries
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Opinosis: A graph based approach to abstractive summarization of highly redundant opinions
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Semantic graph reduction approach for abstractive text summarization
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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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Sumit Chopra, Michael Auli, and Alexander M Rush. 2016 · 2016
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Alex M Lamb, Anirudh Goyal Alias Parth Goyal, Ying Zhang, Saizheng Zhang, Aaron C Courville, and Yoshua Bengio. 2016 · 2016
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Ça glar Gulçehre, and Bing Xiang. 2016 · 2016
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Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio. 2017 · 2017
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Preksha Nema, Mitesh M Khapra, Anirban Laha, and Balaraman Ravindran. 2017 · 2017
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Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Google’s neural machine translation system: Bridging the gap between human and machine translation
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An actor-critic algorithm for sequence prediction
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Beyond bleu: Training neural machine translation with semantic similarity
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