Advanced Techniques for Efficient Text Summarization

Project Code :TCMAPY1135

Objective

Extracting data from publication reports is a standard process in systematic review development. However, the data extraction process still relies too much on manual effort which is slow, costly, and subject to human error. In this study, we developed a text summarization system aimed at enhancing productivity and reducing errors in the traditional data extraction process

Abstract

Text summarization is a crucial natural language processing task, condensing lengthy documents into concise representations while retaining essential information. This article explores advanced techniques for enhancing text summarization efficiency. We delve into extractive and abstractive methods, leveraging machine learning algorithms, neural networks, and pre-trained language models. Additionally, we discuss content weighting, sentence selection, and evaluation metrics to ensure the quality of generated summaries. Our comprehensive overview and insights offer a valuable resource for researchers and practitioners striving to improve the efficacy of automated text summarization systems.

Keywords: Text Summarization, Natural Language Processing.

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