TEXT SUMMARIZATION USING LSA WITH TRANSFORMERS

Abstract

This project endeavors to present an implementation of a text summarization method employing the amalgamation of Latent Semantic Analysis (LSA) with Transformers. The primary objective of the proposed approach is to create a brief summary of an input text while retaining its fundamental meaning. The summarization model is assessed through two metrics, namely BLEU scores and ROUGE scores, which are utilized to gauge the model’s efficacy in generating a succinct and accurate summary. The project comprises several steps, including text preprocessing, feature extraction using LSA, and summary generation using Transformers. The resulting summary is evaluated by comparing it against a reference summary, and the quality of the summary is measured by the BLEU metric and ROUGE scores. The evaluation results reveal that the proposed approach yields high scores on both metrics, indicating its effectiveness in generating precise and concise summaries. Moreover, the project incorporates an analysis of the impact of various parameters on the performance of the summarization model, thereby providing valuable insights into the optimal parameter