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

Computer Science – Computation and Language

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

10.1613/jair.1523

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. 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 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

LexRank: Graph-based Lexical Centrality as Salience in Text Summarization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with LexRank: Graph-based Lexical Centrality as Salience in Text Summarization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and LexRank: Graph-based Lexical Centrality as Salience in Text Summarization will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-38012

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.