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Automatic summarisation is a popular approach to reduce a document to its main arguments.
Machine-Made Index for Technical Literature—An Experiment
Phyllis B Baxendale. 1958 · 1958
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The Automatic Creation of Literature Abstracts
Hans Peter Luhn. 1958 · 1958
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Extractive Summarization by Maximizing Semantic Volume
Dani Yogatama, Fei Liu, and Noah A Smith. 2015 · 1966
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A Trainable Document Summarizer
Julian Kupiec, Jan Pedersen, and Francine Chen. 1995 · 1995
Earlier work this paper cites.
Automatic Analysis, Theme Generation, and Summarization of Machine-Readable Texts
Gerard Salton, James Allan, Chris Buckley, and Amit Singhal. 1996 · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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The duc summarization evaluations
Donna Harman and Paul Over. 2002 · 2002
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Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status
Simone Teufel and Marc Moens. 2002 · 2002
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Using TF-IDF to Determine Word Relevance in Document Queries
Juan Ramos, Juramos Eden, and Rutgers Edu. 2003 · 2003
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Event-Based Extractive Summarization
Elena Filatova and Vasileios Hatzivassiloglou. 2004 · 2004
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ROUGE: A Package for Automatic Evaluation of Summaries
C Y Lin. 2004 · 2004
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TextRank: Bringing order into texts
Rada Mihalcea and Paul Tarau. 2004 · 2004
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LexRank : Graph-based Centrality as Salience in Text Summarization
Dragomir R Radev. 2004 · 2004
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MEAD-A Platform for Multidocument Multilingual Text Summarization
Dragomir R Radev, Timothy Allison, Sasha Blair-Goldensohn, John Blitzer, Arda Celebi, Stanko Dimitrov, Elliott Drabek, Ali Hakim, Wai Lam, Danyu Liu, et al. 2004 · 2004
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Using Latent Semantic Analysis in Text Summarization
Josef Steinberger and Karel Ježek. 2004 · 2004
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A Compositional Context Sensitive Multi-document Summarizer: Exploring the Factors That Influence Summarization
Ani Nenkova, Lucy Vanderwende, and Kathleen McKeown. 2006 · 2006
Cited alongside, same era.
Automatic summarising: The state of the art
Karen Spärck Jones. 2007 · 2007
Cited alongside, same era.
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Lucy Vanderwende, Hisami Suzuki, Chris Brockett, and Ani Nenkova. 2007 · 2007
Cited alongside, same era.
Overview of the TAC 2008 Update Summarization Task
Hoa Trang Dang and Karolina Owczarzak. 2008 · 2008
Cited alongside, same era.
Overview of the TAC 2009 Summarization Track
HT Dang and K Owczarzak. 2009 · 2009
Cited alongside, same era.
Exploring Content Models for Multi-Document Summarization
Aria Haghighi and Lucy Vanderwende. 2009 · 2009
Summarization Based on Embedding Distributions
Hayato Kobayashi, Masaki Noguchi, and Taichi Yatsuka. 2015 · 2015
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Extraction and generalisation of variables from scientific publications
Erwin Marsi and Pinar Öztürk. 2015 · 2015
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A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Neural Summarization by Extracting Sentences and Words
Jianpeng Cheng and Mirella Lapata. 2016 · 2016
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Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016 · 2016
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Using Machine Learning Methods and Linguistic Features in Single-Document Extractive Summarization
Alexander Dlikman and Mark Last. 2016 · 2016
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Cited alongside, same era.
Sentence-based Summarization of Scientific Documents
W. T. Visser and M .B. Wieling. 2009 · 2009
Cited alongside, same era.
SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles
Su Nam Kim, Olena Medelyan, Min-Yen Kan, and Timothy Baldwin. 2010 · 2010
Cited alongside, same era.
Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers
Sonal Gupta and Christopher Manning. 2011 · 2011
Cited alongside, same era.
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
Richard Socher, Jeffrey Pennington, Eric H Huang, Andrew Y Ng, and Christopher D Manning. 2011 · 2011
Cited alongside, same era.
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Cited alongside, same era.
Unsupervised learning of rhetorical structure with un-topic models
Diarmuid Ó Séaghdha and Simone Teufel. 2014 · 2014
Cited alongside, same era.
Later among the works it cites.
Overview of the CL-SciSumm 2016 Shared Task
Kokil Jaidka, Muthu Kumar Chandrasekaran, Sajal Rustagi, and Min Yen Kan. 2016 · 2016
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MUSEEC: A Multilingual Text Summarization Tool
Marina Litvak, Natalia Vanetik, Mark Last, and Elena Churkin. 2016 · 2016
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Knowledge Extraction and Modeling from Scientific Publications
Francesco Ronzano and Horacio Saggion. 2016 · 2016
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Trainable citation-enhanced summarization of scientific articles
Horacio Saggion, Ahmed Abura’ed, and Francesco Ronzano. 2016 · 2016
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Supervised Keyphrase Extraction as Positive Unlabeled Learning
Lucas Sterckx, Cornelia Caragea, Thomas Demeester, and Chris Develder. 2016 · 2016
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SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
Isabelle Augenstein, Mrinal Kanti Das, Sebastian Riedel, Lakshmi Nair Vikraman, and Andrew McCallum. 2017 · 2017
Closest in time.
Multi-Task Learning of Keyphrase Boundary Classification
Isabelle Augenstein and Anders Søgaard. 2017 · 2017
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Get To The Point: Summarization with Pointer-Generator Networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Detecting (Un)Important Content for Single-Document News Summarization
Yinfei Yang, Forrest Bao, and Ani Nenkova. 2017 · 2017
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