LEXRANK GRAPH-BASED LEXICAL CENTRALITY AS SALIENCE IN TEXT SUMMARIZATION PDF

We test the technique on the problem of Text Summarization TS. Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC evaluation.

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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization TS. Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence.

Save to Library. Create Alert. Launch Research Feed. Share This Paper. Figures, Tables, and Topics from this paper. Figures and Tables. Adjacency matrix Supervised learning Cosine similarity Mutual information Email Eigenvector centrality Word-sense disambiguation Automatic summarization Cluster analysis Semi-supervised learning Natural language processing Graph abstract data type Semiconductor industry Money Word sense Heuristic Information processing Computer-mediated communication Principle of good enough Signal-to-noise ratio Entity Spamming Algorithm Attachments Upsampling.

Citations Publications citing this paper. Graph-based term weighting for text categorization Fragkiskos D. A complex network approach to text summarization Lucas Antiqueira , Osvaldo N. Simske Computer Science DocEng '11 Radev Learning with fuzzy hypergraphs: A topical approach to query-oriented text summarization Hadrien Van Lierde , Tommy W. Chow Mathematics, Computer Science Inf. References Publications referenced by this paper.

Brandow , K. Mitze , Lisa F. Rau Computer Science Inf. Generating natural language summaries from multiple on-line sources Dragomir R. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License.

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"LexRank: Graph-based Lexical Centrality as Salience in Text Summarization"

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization TS. Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence.

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LexRank: Graph-based Lexical Centrality as Salience in Text Summarization

LexRank: graph-based lexical centrality as salience in text summarization Published on Jul 1, in Journal of Artificial Intelligence Research 1. Gunes Erkan 12 Estimated H-index: Estimated H-index: Find in Lib. Add to Collection. We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing.

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LexRank: graph-based lexical centrality as salience in text summarization

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