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This repository contains various models for text summarization tasks. Each model has a separate directory containing the implementation code, pretrained weights, and a Jupyter notebook for testing the model on sample input texts. Feel free to use these models for your own text summarization tasks or to experiment with them further.

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Tuhin-SnapD/Text-Summarization-Models

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Text-Summarization-Models

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.

Evaluation

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.

Models

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

Existing

Pegasus

Contributing

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.

Acknowledgements

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.

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This repository contains various models for text summarization tasks. Each model has a separate directory containing the implementation code, pretrained weights, and a Jupyter notebook for testing the model on sample input texts. Feel free to use these models for your own text summarization tasks or to experiment with them further.

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