EBPGA: Extractive Text Summarization Using Binary Particle Swarm Optimization and Masked Genetic Algorithm

Automatic text summarization (ATS) deals with compressing a long document into a shorter version, retaining the key ideas of the original document. It aims to tackle the problem of information overload resulting from the continuous creation of documents. ATS helps people make news feeds, meeting reports, summarized legal and financial documents, study tools, article outlines, social media analysis, and so on. Extractive text summarization (ETS) is a type of ATS that creates summaries (extracts) by choosing sentences from the source without modifying the semantics and structure of the sentences. The main problem in ETS is to select the appropriate sentences that will constitute a coherent, concise, and nonredundant summary that covers the entire document. The literature suggests many optimization-based algorithms for the selection problem. Some authors have used binary particle swarm optimization (BPSO), genetic algorithm (GA), and artificial bee colony (ABC) algorithm to address the selection problem. However, these algorithms suffer from the problem of premature or slow convergence. This article proposes an ETS algorithm using BPSO and masked GA (EBPGA). The novelty of the proposed system is the usage of the masked GA (MGA), which leverages the properties of the document, namely, coherence, redundancy, relevance, and coverage factors, along with domain-specific mutation and crossover to tackle the premature convergence of the BPSO. Thus, the EBPGA algorithm selects the best sentences covering all the topics of the original document to create the summary that is coherent, relevant, and nonredundant. The proposed algorithm was evaluated by producing summaries of size 20%, 30%, and 40%, of the original document, and compared with the existing algorithms. The proposed summarization algorithm obtained a Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score that is comparable with the contemporary algorithms.