Extractive single document summarization using multi-objective modified cat swarm optimization approach: ESDS-MCSO

As the world is progressing faster, to compete with the demand, the need for proficient computing technology has increased, resulting in huge volumes of data. Consequently, the extraction of relevant information from such a massive volume of data in a short time becomes challenging. Hence, automatic text summarization (TS) has emerged as an efficient solution to this problem. In the current study, the automatic TS problem is formulated as a multi-objective optimization problem, and to mitigate this problem, the modified cat swarm optimization (MCSO) strategy is employed. In this work, the population is represented as a collection of feasible individuals where the summary length limit is considered as a constraint that determines the feasibility of an individual. Here, each individual is shaped by randomly selecting some of the sentences encoded in the binary form. Furthermore, two objective functions, namely "coverage and informativeness" and "anti-redundancy," are used to evaluate each individual's fitness. Also, to update the position of an individual, genetic and bit manipulating operators and the best cat memory pool have been incorporated into the system. Finally, from the generated non-dominated optimal solutions, the best solution is selected based on the ROUGE score for the summary generation process. The system's performance is evaluated using ROUGE-1 and ROUGE-2 measures on two standard summarization datasets, namely DUC-2001 and DUC-2002, which revealed that the proposed approach achieved a noticeable improvement in ROUGE scores compared to many state-of-the-art methods mentioned in this paper. The system is also evaluated using the generational distance, CPU processing time, and cohesion, reflecting that the obtained summaries are readable, concise, and relevant being fast converging.