akashkathole7/text_summarisation

Text_Summarisation

This repository contains code and resources related to text summarization, a process of generating a concise and meaningful summary of a longer text. Text summarization has many applications, such as summarizing news articles, scientific papers, and legal documents. By using natural language processing techniques and machine learning algorithms, text summarization models can effectively identify the most important information in a text and generate a summary that captures the essence of the original text.

The purpose of this project is to provide a starting point for those interested in developing or implementing text summarization models. This project includes sample code and datasets to help individuals get started with building their own models.

Datasets This project includes several datasets that can be used to train and test text summarization models. These datasets are publicly available and can be downloaded from reputable sources such as the Cornell University Library.

Code The code included in this project is written in Python and uses popular natural language processing libraries such as NLTK and spaCy. The code provides examples of how to preprocess the data, build and evaluate different text summarization models, and visualize the output. Additionally, the code includes functions for hyperparameter tuning and model evaluation.

Resources This project includes a list of resources related to text summarization, such as research papers, blog posts, and tutorials. These resources can be helpful for individuals looking to learn more about the topic or for those seeking additional datasets or tools to use in their own projects.

Contributing Contributions to this project are welcome. If you have suggestions for additional datasets, resources, or code examples, please submit a pull request. Additionally, please report any issues or bugs you encounter while using the code or datasets.

License This project is licensed under the MIT License. Please see the LICENSE file for more details.

Acknowledgments This project was inspired by the work of many researchers and practitioners in the field of natural language processing and text summarization. We would like to acknowledge their efforts and contributions.

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