TEXT SUMMARIZATION USING LSA WITH TRANSFORMERS
Date
2023-03
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
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
Description
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.
Keywords
Latent Semantic Analysis (LSA),, Transformers,, Text summarization,