Fetching the paper…
Reading the bibliography…
As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches.
Neural extractive text summarization with syntactic compression
Jiacheng Xu and Greg Durrett. 2019 · 1902
Earlier work this paper cites.
Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs
Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, and Bowen Zhou. 2019 · 1905
Earlier work this paper cites.
Exploring domain shift in extractive text summarization
Danqing Wang, Pengfei Liu, Ming Zhong, Jie Fu, Xipeng Qiu, and Xuanjing Huang. 2019a · 1908
Earlier work this paper cites.
Discourse-aware neural extractive model for text summarization
Jiacheng Xu, Zhe Gan, Yu Cheng, and Jingjing Liu. 2019 · 1910
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime G Carbonell and Jade Goldstein. 1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al. 1998 · 1998
Earlier work this paper cites.
Automatic evaluation of summaries using n-gram co-occurrence statistics
Chin-Yew Lin and Eduard Hovy. 2003 · 2003
Earlier work this paper cites.
Lexrank: Graph-based lexical centrality as salience in text summarization
Günes Erkan and Dragomir R Radev. 2004 · 2004
Earlier work this paper cites.
Textrank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
Earlier work this paper cites.
The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
Earlier work this paper cites.
Document summarization method based on heterogeneous graph
Yang Wei. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Cited alongside, same era.
Learning-based single-document summarization with compression and anaphoricity constraints
Greg Durrett, Taylor Berg-Kirkpatrick, and Dan Klein. 2016 · 2016
Cited alongside, same era.
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
Cited alongside, same era.
A survey of heterogeneous information network analysis
Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S Yu Philip. 2016 · 2016
Cited alongside, same era.
Banditsum: Extractive summarization as a contextual bandit
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018 · 2018
Later among the works it cites.
Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
Later among the works it cites.
Adapting the neural encoder-decoder framework from single to multi-document summarization
Logan Lebanoff, Kaiqiang Song, and Fei Liu. 2018 · 2018
Later among the works it cites.
Generating wikipedia by summarizing long sequences
Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. 2018 · 2018
Later among the works it cites.
Ranking sentences for extractive summarization with reinforcement learning
Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018
Later among the works it cites.
Neural latent extractive document summarization
Xingxing Zhang, Mirella Lapata, Furu Wei, and Ming Zhou. 2018 · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Cited alongside, same era.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 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.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Graph-based neural multi-document summarization
Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, and Dragomir Radev. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
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.
Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019 · 2019
Later among the works it cites.
Heterogeneous graph attention networks for semi-supervised short text classification
Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, and Xiaoli Li. 2019 · 2019
Later among the works it cites.
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019b · 2019
Later among the works it cites.
Single document summarization as tree induction
Yang Liu, Ivan Titov, and Mirella Lapata. 2019 · 2019
Later among the works it cites.
Reading like her: Human reading inspired extractive summarization
Ling Luo, Xiang Ao, Yan Song, Feiyang Pan, Min Yang, and Qing He. 2019 · 2019
Later among the works it cites.
Extractive summarization as text matching
Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuan-Jing Huang. 2020 · 2020
Closest in time.