The exponentially evolving size of information today has made it difficult to find relevant information quickly and efficiently. A good extractive text summarizer not only provides the most significant information from the document but also helps the user to decide the relevance of the information. The proposed method is a knowledge-based, generic, extractive text summarization technique. Our approach is based on the centrality of a sentences in the graphical representation of the documents. The graph is constructed using the pair-wise softcosine similarity measures between the sentences derived using the S semantic relations presented in WordNet lexical database. Eigenvector centrality measure outperforms the weighted degree, betweenness and closeness centrality measures. The resultant summary is compared against the gold-standard summaries of BBC news articles from year 2004 to 2005 and DUC 2007 datasets. The ROUGE-I, -2 and -Lmetrices are used to evaluate the results and found that our approach performs better than LexRank, TextRank, Luhn and LSA baseline text summarizers.


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    Title :

    Automatic Text Summarization using Soft-Cosine Similarity and Centrality Measures


    Contributors:


    Publication date :

    2020-11-05


    Size :

    452403 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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