/Text-Summarizer

Primary LanguageJupyter NotebookMIT LicenseMIT

Text-Summarizer

Overview

The Text Summarizer project aims to develop a powerful and efficient tool for generating concise summaries from large bodies of text. Leveraging advanced natural language processing (NLP) techniques, this system offers both extractive and abstractive summarization capabilities. The goal is to assist users in quickly understanding the key points from extensive documents, articles, or any text data, making it particularly useful for research, news analysis, and educational purposes.

Features

  1. Extractive Summarization: Identifies and extracts the most important sentences from the text to create a summary.
  2. Abstractive Summarization: Generates a new summary that paraphrases and condenses the text while retaining the core message.
  3. Multi-lingual Support: Capable of summarizing text in multiple languages.
  4. User-Friendly Interface: A simple and intuitive user interface for easy input and output management.
  5. Real-Time Processing: Fast and efficient summarization for immediate results.

Technology Stack

  1. Programming Language: Python
  2. Libraries & Frameworks:
    • Natural Language Toolkit (NLTK)
    • Transformers (Hugging Face)
    • TensorFlow/Keras
    • BeautifulSoup
  3. Deployment: Flask/Django for the web application, Docker for containerization

Installation

Prerequisites

  • Python 3.7 or higher
  • Pip (Python package installer)

Install Core Dependencies

Python Libraries

  • numpy: For numerical operations.

  • pandas: For data manipulation and analysis.

  • scikit-learn: For machine learning algorithms and utilities.

  • Natural Language Processing

  • nltk: Natural Language Toolkit, useful for text processing and analysis.

  • spacy: Industrial-strength NLP with pre-trained models and easy-to-use API.

  • transformers: Hugging Face library for state-of-the-art NLP models like BERT, GPT, etc.

  • gensim: Topic modeling and document similarity analysis.

Deep Learning Frameworks

  • tensorflow or pytorch: For deep learning model implementation and training.
  • keras: A high-level API for building and training deep learning models (if using TensorFlow).

Text Preprocessing

  • beautifulsoup4: For web scraping to gather text data.
  • lxml: XML and HTML processing.

Summarization Specific

  • sumy: A simple and effective library for extractive summarization.
  • t5: Implementation of Google’s T5 model for abstractive summarization (part of the transformers library).

Usage

  • Upload Text: Use the interface to upload a text file or paste the text you want to summarize.
  • Choose Summarization Type: Select between extractive or abstractive summarization.
  • Set Parameters: (Optional) Adjust the summary length and other settings.
  • Generate Summary: Click the 'Summarize' button to get the summary.
  • Download/Copy Summary: Download the generated summary or copy it to your clipboard.

Project structure

text-summarizer/ ├── data/ │ └── example_texts/ ├── models/ │ └── pretrained/ ├── static/ │ └── css/ │ └── js/ ├── templates/ │ └── index.html ├── notebooks/ │ └── Text_summarizer.ipynb ├── app.py ├── requirements.txt └── README.md

License

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

Contact

For any inquiries or issues, please contact:

Show Your Support

If you find this project helpful or interesting, please consider showing your support! You can:

  • Star the repository on GitHub.
  • Follow the repository for updates on new features and releases.
  • Share the project with others who might benefit from it.
  • For more projects and updates, follow my GitHub profile @yourusername.
  • Share your experience and how this tool has helped you.

Your support helps me continue to improve the project and make it better for everyone. Thank you for your support!