scholarly journals EVALUATING THE IMPORTANCE OF BRAZILIAN PORTS USING GRAPH CENTRALITY MEASURES

2020 ◽  
Vol 40 ◽  
Author(s):  
Luana Carneiro Brandão ◽  
Renata Raposo Del-Vecchio ◽  
João Carlos Correia Baptista Soares de Mello ◽  
Cristiane Nunes Francisco
Keyword(s):  
Centrality Measures ◽  
Graph Centrality ◽  
Brazilian Ports

Text Summarisation Using Laplacian Centrality-Based Minimum Vertex Cover

2019 ◽  
Vol 18 (04) ◽  
pp. 1950050
Author(s):  
Anand Gupta ◽  
Manpreet Kaur
Keyword(s):  
State Of The Art ◽  
Vertex Cover ◽  
Centrality Measures ◽  
Common Goal ◽  
Graph Centrality ◽  
Centrality Measure ◽  
Local Connectedness ◽  
Source Document ◽  
Modified Method ◽  
The Common

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.

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