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

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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Our LexRank implementation requires thecosine similarity threshold, 0. Weighted cosine similarity graph for the cluster in Figure 1.

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

Considering the relatively low complexity of degree centrality, it stillserves as a plausible alternative when one needs a simple implementation. Multi-document summarization by graph search and matching. Grapu-based can normalize the row sumsof the corresponding transition matrix so that we have a stochastic matrix.

The top scores we have got in all data sets come from our new methods. This is a measure of how close the sentence is to the centroid of the cluster. The pagerank citation ranking: A straightforward way of formulating this ideais to consider every node having a centrality value and distributing this centrality to itsneighbors.

Figure 3 ceentrality the graphs that correspond to the adjacency matrices derived by assumingthe pair of sentences that have a similarity above 0. 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. Using Maximum Entropy for Sentence Extraction.

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Spectral clustering for German verbs – C, Walde, et al. A threshold value is used to filter out the relationships between sentences whose weights are fall below the threshold. We have introduced three dif-ferent methods for computing centrality in similarity graphs.

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

We tedt methods to compute centrality using the similarity graph. A common way of assessing word centrality is tolook at the centroid of the document cluster in a vector space.

Reranker penalizes the sentences thatare similar to the sentences already included in the summary so that a better informationcoverage is achieved. Note that Degree centrality scores are also computed inthe Degree array as a side product of the algorithm.

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

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. The result is a subset of the similarity graph, from where we can pick one node that has the highest number of degree.

All the feature values are normalized so that the sentencethat has the highest value gets the score 1, and the sentence with the lowest value getsthe score 0. Extractive TS relies on the concept of sentence salienceto identify the most important sentences in a document or set of documents.

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In this paper, a new method of determining the most important sentences in a given corpus was discussed. Graph-based Lexical Centrality as Salience in Text Summarization scores for a given set of sentences. A Markov chain is irreducible if any state is reachable from any other state, i. Adjacency matrix Search for additional papers on this topic. Unlike our system, the studies mentioned above do not make use of any heuristic features of the sentences other than the centrality score. A cluster of documents can be viewed as a network of sentences that are related to each other.

In this research, they measure similarity between sentences by considering every sentence as bag-of-words model. This similarity measure is then used to build a similarity matrix, which can be used as a similarity graph between sentences. However, in many types of social networks, not all of the relationshipsare considered equally important.

Power Method for computing the stationary distribution of a Markovchain. Second, the feature vector is converted toa scalar value using the combiner. Statisticsbased summarization – step one: