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We propose a novel reinforcement learning based framework PoBRL for solving multi-document summarization.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams. 1992 · 1992
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The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime Carbonell and Jade Goldstein. 1998 · 1998
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ROUGE: A package for automatic evaluation of summaries
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Duc 2005: Evaluation of question-focused summarization systems
Hoa Trang Dang. 2006 · 2005
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A study of global inference algorithms in multi-document summarization
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Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2017 · 2017
Adapting the neural encoder-decoder framework from single to multi-document summarization
Logan Lebanoff, Kaiqiang Song, and Fei Liu. 2018 · 2018
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Actor-critic based training framework for abstractive summarization
Piji Li, Lidong Bing, and Wai Lam. 2018 · 2018
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Multi-reward reinforced summarization with saliency and entailment
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Ranking sentences for extractive summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Adapting neural single-document summarization model for abstractive multi-document summarization: A pilot study
Jianmin Zhang, Jiwei Tan, and Xiaojun Wan. 2018 · 2018
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Mix and match agent curricula for reinforcement learning
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Banditsum: Extractive summarization as a contextual bandit
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Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
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Multi-granularity interaction network for extractive and abstractive multi-document summarization
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Extractive summarization as text matching
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