Graph-Based Text Summarization and Its Application on COVID-19 Twitter Data

Abstract

Large volumes of structured and semi-structured data are being generated every day. Processing this large amount of data and extracting important information is a challenging task. The goal of an automatic text summarization is to preserve the key information and the overall meaning of the article to be summarized. In this paper, a graph-based approach is followed to generate an extractive summary, where sentences of the article are considered as vertices, and weighted edges are introduced based on the cosine similarities among the vertices. A possible subset of maximal independent sets of vertices of the graph is identified with the assumption that adjacent vertices provide sentences with similar information. The degree centrality and clustering coefficient of the vertices are used to compute the score of each of the maximal independent sets. The set with the highest score provides the final summary of the article. The proposed method is evaluated using the benchmark BBC News data to demonstrate its effectiveness and is applied to the COVID-19 Twitter data to express its applicability in topic modeling. Both the application and comparative study with other methods illustrate the efficacy of the proposed methodology.

Ensemble Text Summarization Model for COVID‐19‐Associated Datasets

“…Te numerator 􏽐 o 􏽐 p loops through all bigrams in a single original summary and calculates the number of times an overlapping (matching) bigram is found in the candidate summary. Tis process of calculating the score is repeated for the overall reference summaries present in our test set [7,55]. Te denominator simply counts the total number of bigrams in all reference summaries.…”