2017

Graph-based Neural Multi-Document Summarization

Yasunaga, Michihiro, Zhang, Rui, Meelu, Kshitijh et al.

Understand

We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs.

We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features.
Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation.
We then use a greedy heuristic to extract salient sentences while avoiding redundancy.

Built on

Rhetorical structure theory: Toward a functional theory of text organization
William C Mann and Sandra A Thompson. 1988 · 1988
Earlier work this paper cites.
A common theory of information fusion from multiple text sources step one: cross-document structure
Dragomir R Radev. 2000 · 2000
Earlier work this paper cites.
Sentence ordering in multidocument summarization
Regina Barzilay, Noemie Elhadad, and Kathleen R McKeown. 2001 · 2001
Earlier work this paper cites.
Newsinessence: A system for domain-independent, real-time news clustering and multi-document summarization
Dragomir R Radev, Sasha Blair-Goldensohn, Zhu Zhang, and Revathi Sundara Raghavan. 2001 · 2001
Earlier work this paper cites.
Statistical mechanics of complex networks
Réka Albert and Albert-László Barabási. 2002 · 2002
Earlier work this paper cites.
Probabilistic text structuring: Experiments with sentence ordering
Mirella Lapata. 2003 · 2003
Earlier work this paper cites.
Left-brain/right-brain multi-document summarization
John M Conroy, Judith D Schlesinger, Jade Goldstein, and Dianne P O’leary. 2004 · 2004
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.
A language independent algorithm for single and multiple document summarization
Rada Mihalcea and Paul Tarau. 2005 · 2005
Earlier work this paper cites.
Topic-focused multi-document summarization using an approximate oracle score
John M Conroy, Judith D Schlesinger, and Dianne P O’Leary. 2006 · 2006
Earlier work this paper cites.
A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization
Ani Nenkova, Lucy Vanderwende, and Kathleen McKeown. 2006 · 2006
Earlier work this paper cites.
Modeling and evaluating summaries using complex networks
Thiago Pardo, Lucas Antiqueira, Maria Nunes, Osvaldo Oliveira, and Luciano da Fontoura Costa. 2006 · 2006
Earlier work this paper cites.
Improved affinity graph based multi-document summarization
Xiaojun Wan and Jianwu Yang. 2006 · 2006
Earlier work this paper cites.
Multi-document summarization using support vector regression
Sujian Li, You Ouyang, Wei Wang, and Bin Sun. 2007 · 2007
Earlier work this paper cites.
A study of global inference algorithms in multi-document summarization
Ryan McDonald. 2007 · 2007
Earlier work this paper cites.

Similar

A complex network approach to text summarization
Lucas Antiqueira, Osvaldo N Oliveira, Luciano da Fontoura Costa, and Maria das Graças Volpe Nunes. 2009 · 2009
Cited alongside, same era.
A scalable global model for summarization
Dan Gillick and Benoit Favre. 2009 · 2009
Cited alongside, same era.
Exploring content models for multi-document summarization
Aria Haghighi and Lucy Vanderwende. 2009 · 2009
Cited alongside, same era.
Divrank: the interplay of prestige and diversity in information networks
Qiaozhu Mei, Jian Guo, and Dragomir Radev. 2010 · 2010
Cited alongside, same era.
Classy 2011 at tac: Guided and multi-lingual summaries and evaluation metrics
John M Conroy, Judith D Schlesinger, Jeff Kubina, Peter A Rankel, and Dianne P O’Leary. 2011 · 2011
Cited alongside, same era.
Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval. 2011 · 2011
Cited alongside, same era.
An assessment of the accuracy of automatic evaluation in summarization
Karolina Owczarzak, John M Conroy, Hoa Trang Dang, and Ani Nenkova. 2012 · 2012
Cited alongside, same era.
Towards coherent multi-document summarization
Janara Christensen, Mausam, Stephen Soderland, and Oren Etzioni. 2013 · 2013
Cited alongside, same era.
Using supervised bigram-based ilp for extractive summarization
Chen Li, Xian Qian, and Yang Liu. 2013 · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Cited alongside, same era.
Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.

Then

Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Later among the works it cites.
Ranking with recursive neural networks and its application to multi-document summarization
Ziqiang Cao, Furu Wei, Li Dong, Sujian Li, and Ming Zhou. 2015 · 2015
Later among the works it 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
Later among the works it cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2015 · 2015
Later among the works it cites.
A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Later among the works it cites.
Optimizing sentence modeling and selection for document summarization
Wenpeng Yin and Yulong Pei. 2015 · 2015
Later among the works it cites.
Attsum: Joint learning of focusing and summarization with neural attention
Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei, and Yanran Li. 2016 · 2016
Later among the works it cites.
Neural summarization by extracting sentences and words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
Later among the works it cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Later among the works it cites.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
Later among the works it cites.
Neural network-based abstract generation for opinions and arguments
Lu Wang and Wang Ling. 2016 · 2016
Later among the works it cites.
Improving multi-document summarization via text classification
Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2017 · 2017
Closest in time.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2017 · 2017
Closest in time.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017 · 2017
Closest in time.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…