This repository contains Python implementations of various text summarization models. Text summarization is the process of generating a shorter version of a longer text while preserving its most important information. It has many practical applications, such as summarizing news articles or academic papers, and can be used to save time and improve comprehension.

To evaluate the performance of the models, we have used the ROUGE metric (Recall-Oriented Understudy for Gisting Evaluation), which is commonly used for evaluating the quality of automatic summarization.

The repository contains several different 18 text summarization models. Apart from this 12 more Pegasus models were implemented to compute the scores, and 1 Novel Graph Method was also implemented

Contributions to this repository are welcome! If you have an idea for a new summarization model or an improvement to an existing one, feel free to create a pull request.

This repository was created by Tuhin, Anant and Gokul as part of Academic Capstone Project. We would like to thank Prof. Durgesh Kumar and Multiple learned faculties of Vellore Institute of technology.