LexPageRank: Prestige in Multi-Document Text Summarization

By Günes Erkan, Dragomir R. Radev. Conference on Empirical Methods in Natural Language Processing, 2004

340 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e6a4c9c918a4d8154b08bbdc3673da6a6eaeba3c

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Citations in the corpus

Random-Walk Term Weighting for Improved Text Classification (2007, background): “, 2003), citation analysis, and more recently in several text processing applications (Mihalcea and Tarau, 2004), (Erkan and Radev, 2004).”
Explorations in Automatic Book Summarization (2007, methodology): “, 1997) or eigenvector centrality (Mihalcea and Tarau, 2004; Erkan and Radev, 2004; Wolf and Gibson, 2004).”
Random-Walk Term Weighting for Improved Text Classification (2006, background): “, 2003), citation analysis, and more recently in several text processing applications (Mihalcea and Tarau, 2004), (Erkan and Radev, 2004).”
Evolutionary timeline summarization: a balanced optimization framework via iterative substitution (2011, methodology): “Tex-tRank [13] and LexPageRank [3] use algorithms similar to PageRank and HITS to compute sentence importance.”
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization (2004, methodology): “After the DUC evaluations, a more detailed analysis and more careful implementation of the method was presented together with a comparison against degree centrality and centroid-based summarization ( Erkan & Radev, 2004a ).”
Extractive Summarization Using Supervised and Semi-Supervised Learning (2008, background): “Erkan and Radev (2004) and Yoshioka (2004) evaluate the relevance (similarity) between any two sentences first.”
Multi-document summarization using cluster-based link analysis (2008, methodology): “LexPageRank [4] is an approach for computing sentence importance based on the concept of eigenvector centrality.”
Cross-Language Document Summarization Based on Machine Translation Quality Prediction (2010, methodology): “The summary sentences can also be selected by using machine learning methods (Kupiec et al., 1995; Amini and Gallinari, 2002) or graph-based methods (ErKan and Radev, 2004; Mihalcea and Tarau, 2004).”
Cross-language document summarization via extraction and ranking of multiple summaries (2019, methodology): “centroid-based method [20], integer linear programming (ILP) [9], submodular function maximization [13–15], graph-based methods [5,24], and supervised learning-based methods [4,17,18,21].”
Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources (2007, methodology): “Graph-based methods have also been employed (Erkan and Radev, 2004a; Erkan and Radev, 2004b; Mihalcea, 2005; Mihalcea and Tarau, 2005; Mihalcea and Radev, 2006).”
LexRank: Graph-based Centrality as Salience in Text Summarization (2004, methodology): “After the DUC evaluations, a more detailed analysis and more careful implementation of the method was presented together with a comparison against degree centrality and centroid-based summarization ( Erkan & Radev, 2004a ).”