Text Summarisation Using Laplacian Centrality-Based Minimum Vertex Cover

Anand Gupta () and Manpreet Kaur
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Anand Gupta: Department of Computer and Engineering, Netaji Subhas University of Technology, New Delhi, India
Manpreet Kaur: Department of Computer and Engineering, Netaji Subhas University of Technology, New Delhi, India

Journal of Information & Knowledge Management (JIKM), 2019, vol. 18, issue 04, 1-20

Abstract: Outdegree Centrality (OC) is a graph-based centrality measure that captures local connectedness of a node in a graph. The measure has been used in the literature to highlight key sentences in a graph-based optimisation method for summarisation. It is observed in resultant summaries that OC tends to be biased towards selecting introductory sentences of the document producing only generic summaries. The different graph centrality measures lead to different interpretations of a summary. Therefore, the authors propose to use another suitable centrality measure in order to generate more specific summary rather than a generic summary. Such a summary is expected to be highly informative covering all the subtopics of the source document. This requirement has instigated the authors to use Laplacian Centrality (LC) measure to find the significance of the nodes. The essence of this measure lies in highlighting central nodes from subgraphs which contribute non-uniformly towards the common goal of the graph. The modified method has shown significant improvement in informativeness and coherence of summaries and outperformed state-of-the-art results.

Keywords: Information retrieval; text summarisation; node centrality measures; Laplacian centrality; coherence (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219649219500503

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