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We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents.
The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime Carbonell and Jade Goldstein · 1998
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Lexrank: Graph-based lexical centrality as salience in text summarization
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A study of global inference algorithms in multi-document summarization
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Teaching machines to read and comprehend
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Topical coherence for graph-based extractive summarization
Daraksha Parveen, Hans-Martin Ramsl, and Michael Strube · 2015
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A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston · 2015
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Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Extractive summarization using continuous vector space models
Mikael Kageback, Olof Mogren, Nina Tahmasebi, and Devdatt Dubhashi · 2014
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang
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Sequence-to-sequence rnns for text summarization
Ramesh Nallapati, Bowen Zhou, and Bing Xiang
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Optimizing sentence modeling and selection for document summarization
Wenpeng Yin and Yulong Pei · 2015
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Attsum: Joint learning of focusing and summarization with neural attention
Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei · 2016
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Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata · 2016
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