Sentence Selection for Extractive Text Summarization using TOPSIS Approach

Abstract

In the present era, it is challenging for people to extract crucial information due to the ongoing evolution of textual data on the internet as well as in online resources. Thus, only a procedure known as Text Summarization (TS) can be used to acquire the required information. Therefore, the most common and practical method of summary is known as extractive text summarization, which involves selecting the most pertinent sentences from the original text material. In a relatively short period of time, it can gather the most priceless information. In order to extract the phrases that serve as a document's summary, this study applies scoring-based optimisation models employing Ant Colony Optimisation (ACO), Butterfly Optimisation (BO), and Particle Swarm Optimisation (PSO). The model is simulated on the DUC 2006 dataset, and the TOPSIS approach is used to validate the results for various DUC documents. The PSO-based summarizer, however, performs noticeably better than the other two summarizers based on rank.