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.
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A brief summary of “LexRank: Graph-based Lexical Centrality as Salience in Text Summarization”
This method works firstly by generating a graph, composed of all sentences in the corpus. We can normalize the row sumsof the corresponding transition matrix so that we have a stochastic matrix. We call this new measure of sentencesimilarity lexical PageRank, or LexRank.
At each iteration, the eigenvector isupdated by multiplying with the transpose of graph-basde stochastic matrix.
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization – Semantic Scholar
This paper has highly influenced other papers. Constructing the similarity graph of sentences provides us witha better view of important sentences compared to the centroid approach, which is prone szlience of the information in a document cluster.
Association for Computational Linguistics. In this paper, we will take graph-based methods in NLP one step further. In LexRank, we have tried to make use of more of theinformation in the graph, and got even better results in most of the cases. A Markov chain is irreducible if any state is reachable from any other state, i.
Graph-based Lexical Centrality as Salience in Text Summarization In Section 2, we present centroid-based summarization, a well-known method for judging sentence centrality. The pagerank citation ranking: This can be seen in Figure 1 wherethe majority of the values in the similarity matrix are nonzero.
Since the Markovchain ,exical irreducible and aperiodic, the algorithm is guaranteed to terminate. In this research, they measure similarity between sentences by considering every sentence as bag-of-words model. We discussseveral methods to compute centrality using the similarity graph.
DUC data sets are perfectly clusteredinto related documents by human assessors.
Training a Selection Function for Extraction. Spectral clustering for German verbs – C, Walde, traph-based al. The result is a subset of the similarity graph, from where we can pick one node that has the highest number of degree. Recently, robust graphbased methods for Graaph-based have also been gaini The similaritycomputation might be improved by incorporating more features e.
Graph-based Lexical Centrality as Salience in Text Summarization in the unrelated document to be summaruzation in a generic summary of the cluster. An eigenvector centrality method can then associate a probability with each object labeled or unlabeled. Our summarization approach in this paper is to assess the centrality of each sentence in a cluster and extract the most important ones to include in the summary. However, in many types of social networks, not all of the relationshipsare considered equally important.
Using Maximum Entropy for Sentence Extraction. There is an edge from a term t to a sentence s if t occurs in s. Toolow thresholds may mistakenly take weak similarities into consideration while too highthresholds may lose many of the similarity relations in a cluster.
This is due to summarizatin fact that the problems in abstractive summarization, suchas semantic representation, inference and natural language generation, are relatively hardercompared to a data-driven approach such as sentence extraction.
CiteSeerX — Lexrank: Graph-based lexical centrality as salience in text summarization
Our LexRank implementation requires thecosine similarity threshold, 0. A Flexible Clustering Tool for Summarization. Foreach word that occurs in a sentence, the value of the corresponding dimension in the vectorrepresentation of the grxph-based is the number of occurrences of the word in the sentencetimes the idf of the word. A cluster of documents can be viewed as a network of lexeank that are related to each other.
Prestige in multi-document text sum- marization. For example, the words that are likely to occur in alm In thisframework, these features serve as intermediate nodes on a path from unlabeled to labelednodes. Centroid Graph-based centrality has several advantages over Centroid.
Although summaries produced by humans are typicallynot extractive, most of the summarization research today is on extractive summarization. In all of the runs, we have used Length and Position features of MEAD assupporting heuristics in addition to our centrality features.