LexRank: Graph-based Lexical Centrality as Salience in Text Summarization Degree Centrality In a cluster of related documents, many of the sentences are. A brief summary of “LexRank: Graph-based Lexical Centrality as Salience in Text Summarization”. Posted on February 11, by anung. This paper was. Lex Rank Algorithm given in “LexRank: Graph-based Lexical Centrality as Salience in Text Summarization” (Erkan and Radev) – kalyanadupa/C-LexRank.
|Published (Last):||20 June 2010|
|PDF File Size:||1.25 Mb|
|ePub File Size:||8.77 Mb|
|Price:||Free* [*Free Regsitration Required]|
The results on the noisy data are given in Table 6. This paper has 1, citations.
In this paper we present a detailed analysis of our approach andapply it to a larger data set including data from earlier DUC evaluations. Early research summarrization extractive summarization is based on simple heuristic features of the sentences such as their position in the text, the overall frequency of lexica, words theycontain, or some key phrases indicating the importance of the sentences Baxendale, ;Edmundson, ; Luhn, DUC data sets are perfectly clusteredinto related documents by human assessors.
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization – Semantic Scholar
On the extreme point where we have a very highthreshold, we would have no edges in the graph so that Degree or LexRank centrality wouldbe of no use.
To solve this problem, Page et al. All the numbers are normalized so thatthe highest ranked sentence gets the score 1. Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents.
A brief summary of “LexRank: Graph-based Lexical Centrality as Salience in Text Summarization”
Approach summarixation evaluation – McKeown, Barzilay, et al. The problem of extracting a sentence that represents the contents of a given document or a collection of documents is known as extractive summarization problem. In Research and Development inInformation Retrieval, pp. All of our three new methods Degree, LexRank with threshold, and continuous LexRank perform significantly better than the ba Adjacency matrix Cosine similarity Natural language processing.
The higher the threshold, the less informative, or even mis-leading, similarity graphs we must have. The results show thatdegree-based methods including LexRank outperform both centroid-based methods andother systems participating in DUC in most of the cases.
The graph-based representation of the relations between natural language constructs provides us with many new ways of information processing with applications to severalproblems such as document clustering, word sense disambiguation, prepositional phraseattachment.
This paper has highly influenced other papers. Semantic Scholar estimates that this publication has 1, citations based on the available data.
A surprising pointis that centroid-based summarization also gives good results although still worse than theothers most of the time. We discuss several methods to compute centrality using the similarity graph. We test the technique on the problemof Text Summarization TS.
We try to avoid the repeated information in thesummaries by using the reranker of the MEAD system. The basic measurement is using TF-IDF formulation, where tem frequency TF contributes to the similarity strength as the number of word occurrences is higher.
The top scores we have got in all data sets come from our new methods. A straightforward way of formulating this ideais to consider every node having a centrality value and distributing this centrality to itsneighbors. Showing of extracted citations.
Spectral clustering for German verbs – C, Walde, et al. We consider a new approach, LexRank, forcomputing sentence importance based on the concept of eigenvector centrality in a graphrepresentation of sentences.
The lexxrank relation we used to construct the graphs can be replaced by anymutual information relation between natural language entities. A threshold value is used to filter out the relationships between sentences whose weights are fall below the threshold.
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
A stochastic, irreducible and aperiodic matrix Minput: Obviously, we would not want any of the sentences Rext Setup In this section, we describe the data set, the evaluation metric and the summarizationsystem we used in our experiments.
In this model, a connectivity matrix based on intra-sentencecosine similarity is used as the adjacency matrix of the graph representation of sentences. Since the Texg is irreducible and aperiodic, the algorithm is guaranteed tect terminate. Weighted cosine similarity graph for the cluster in Figure 1. Table 2 shows the LexRank scoresfor the graphs in Figure 3 setting the damping factor to 0.
This is a measure of howclose the sentence is to the centroid of the cluster. Foreach word that occurs in a sentence, the value of the corresponding dimension in the vectorrepresentation of the sentence is the number of occurrences of the word in the sentencetimes the idf of the word. Unsupervised word sense disambiguation rivaling supervised meth- ods.