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# Text Summarizer

A Natural Language Processing (NLP) project that uses Generative AI to summarize long pieces of text into concise, meaningful summaries.

# Table of Contents

**Introduction**

**Features**

**How it Works**

**Installation**

**Usage**

**Example Use Cases**

**Contributing**

**License**

## Introduction

The Text Summarizer project uses Gen AI to automatically summarize long pieces of text into shorter, more digestible summaries. This can be useful for a variety of applications, such as:

Summarizing news articles or blog posts

Condensing long documents or reports

Generating abstracts for academic papers

## Features

Automatic Summarization: The Text Summarizer uses Gen AI to automatically summarize text without the need for manual intervention.

Customizable Summary Length: Users can specify the desired length of the summary, from a brief abstract to a longer summary.

Support for Multiple File Formats: The Text Summarizer can handle text files in various formats, including .txt, .pdf, and .docx.

## How it Works

The Text Summarizer uses a combination of natural language processing (NLP) and machine learning algorithms to analyze the input text and generate a summary. The process involves:

Text Preprocessing: The input text is preprocessed to remove stop words, punctuation, and other irrelevant information.

Text Analysis: The preprocessed text is then analyzed to identify key phrases, entities, and concepts.

Summary Generation: The analyzed text is then used to generate a summary, based on the user-specified summary length.

## Installation

To install the Text Summarizer, follow these steps:

Clone the repository: git clone https://github.com/NANDAGOPALNG/ML-Nexus/tree/main/Generative%20Models/Text%20Summarizer

Install the required dependencies: pip install -r requirements.txt

Run the Text Summarizer: python text_summarizer.py

## Usage

To use the Text Summarizer, simply run the text_summarizer.py script and follow the prompts. You can specify the input text file, summary length, and other options as needed.

## Example Use Cases

Summarizing a news article: python text_summarizer.py -i news_article.txt -s 100

Summarizing a research paper: python text_summarizer.py -i research_paper.pdf -s 200

## Contributing

Contributions are welcome! If you'd like to contribute to the Text Summarizer project, please fork the repository and submit a pull request.

## License

The Text Summarizer project is licensed under the MIT License. See LICENSE for details

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