From fcce59ce808478b8f9ab242df77559f5138388da Mon Sep 17 00:00:00 2001 From: "NANDA GOPAL.D" Date: Mon, 4 Nov 2024 21:16:53 +0530 Subject: [PATCH] Update README.md --- Generative Models/Text Summarizer/README.md | 76 +++++++++++++++++++++ 1 file changed, 76 insertions(+) diff --git a/Generative Models/Text Summarizer/README.md b/Generative Models/Text Summarizer/README.md index 8b137891..85f5448a 100644 --- a/Generative Models/Text Summarizer/README.md +++ b/Generative Models/Text Summarizer/README.md @@ -1 +1,77 @@ +# 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