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Our analysis of large summarization datasets indicates that redundancy is a very serious problem when summarizing long documents.
The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime Carbonell and Jade Goldstein. 1998 · 1998
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Summarization: (1) using mmr for diversity - based reranking and (2) evaluating summaries
Jade Stewart and Jaime Carbonell. 1998 · 1998
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Graph-based submodular selection for extractive summarization
Hui Lin, Jeff Bilmes, and Shasha Xie. 2009 · 2009
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Tackling redundancy in text summarization through different levels of language analysis
Elena Lloret and Manuel Sanz. 2013 · 2013
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Automatic Text Summarization: Past, Present and Future , pages 3–21. Springer Berlin Heidelberg, Berlin, Heidelberg
Horacio Saggion and Thierry Poibeau. 2013 · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
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Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
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A redundancy-aware sentence regression framework for extractive summarization
Pengjie Ren, Furu Wei, Zhumin Chen, Jun Ma, and Ming Zhou. 2016 · 2016
Cited alongside, same era.
Unsupervised query-focused multi-document summarization using the cross entropy method
Guy Feigenblat, Haggai Roitman, Odellia Boni, and David Konopnicki. 2017 · 2017
Cited alongside, same era.
A deep reinforced model for abstractive summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Learning to score system summaries for better content selection evaluation
Maxime Peyrard, Teresa Botschen, and Iryna Gurevych. 2017 · 2017
Cited alongside, same era.
Neural document summarization by jointly learning to score and select sentences
Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang, Ming Zhou, and Tiejun Zhao. 2018 · 2018
Later among the works it cites.
Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization
Taehee Jung, Dongyeop Kang, Lucas Mentch, and Eduard Hovy. 2019 · 2019
Later among the works it cites.
Analyzing sentence fusion in abstractive summarization
Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, and Fei Liu. 2019 · 2019
Later among the works it cites.
Deep reinforcement learning with distributional semantic rewards for abstractive summarization
Siyao Li, Deren Lei, Pengda Qin, and William Yang Wang. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
Later among the works it cites.
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
Cited alongside, same era.
Content selection in deep learning models of summarization
Chris Kedzie, Kathleen McKeown, and Hal Daumé III. 2018 · 2018
Cited alongside, same era.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2016a
Cited in the paper.
Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gulçehre, and Bing Xiang. 2016b
Cited in the paper.
Yusu Qian, Urwa Muaz, Ben Zhang, and Jae Won Hyun. 2019 · 2019
Later among the works it cites.
Extractive summarization of long documents by combining global and local context
Wen Xiao and Giuseppe Carenini. 2019 · 2019
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AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Keping Bi, Rahul Jha, W. Bruce Croft, and Asli Celikyilmaz. 2020 · 2020
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