Text Summarization Project

Description

This project aims to implement a simple yet effective text summarization tool using Python, leveraging Natural Language Processing (NLP) libraries such as NLTK and spaCy. The tool preprocesses a given text to remove unnecessary characters, tokenizes it into sentences and words, removes stop words, and then applies a frequency-based scoring system to extract the most relevant sentences as the summary.

Features

Text cleaning and preprocessing
Sentence and word tokenization
Stop words removal
Frequency-based importance ranking of sentences
Generation of text summaries
Visualizations including word clouds and frequency distribution plots to analyze the text and summaries

Installation

Clone this repository to your local machine using:

git clone https://github.com/your-username/text-summarization-project.git

Prerequisites

Ensure you have Python installed on your system. This project requires the following Python libraries:

NLTK
spaCy
matplotlib
wordcloud

You can install them using pip:

pip install nltk spacy matplotlib wordcloud

Contributing

Contributions to improve the project are welcome. Please follow these steps to contribute:

Fork the repository.
Create a new branch (git checkout -b feature-branch).
Make your changes and commit them (git commit -am 'Add some feature').
Push to the branch (git push origin feature-branch).
Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.