Text Summarizer | Pratik Savla

A simple TensorFlow implementation of text summarization — both extractive and abstractive — using the NLTK library.

🚀 Overview

This project demonstrates how to build a basic abstractive text summarizer using TensorFlow and Natural Language Toolkit (NLTK). It covers preprocessing, tokenization, and training a sequence-to-sequence model to generate summaries from large blocks of text.

🔍 Whether you're a beginner in NLP or looking to explore sequence models in TensorFlow, this is a great starting point!

🔧 Technologies Used

Python 🐍

TensorFlow 📈

NLTK 📚

📦 Features

Text cleaning and preprocessing using NLTK

Tokenization of source and target text

Seq2Seq model implementation using TensorFlow

Supports both extractive and abstractive summarization

Simple and beginner-friendly codebase

📁 Project Structure

text-summarizer/

├── data/

│ └── sample_articles.txt

├── model/

│ └── summarizer_model.py

├── utils/

│ └── preprocessing.py

├── main.py

└── README.md

▶️ Getting Started

1. Clone the repository

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

cd text-summarizer

2. Install requirements

pip install -r requirements.txt

3. Run the main script

python main.py

📚 Example

Original Text:

Artificial intelligence is transforming the way we live, work, and interact with machines.

Generated Summary:

AI changes how we live and work.

🧠 How it Works

Text Preprocessing: Using NLTK to tokenize, remove stop words, and clean text.

Token Embedding: Text is converted into sequences and padded.

Model Architecture: Encoder-Decoder model using TensorFlow's LSTM layers.

Training: The model is trained on pairs of articles and summaries.

🛠️ Future Improvements

Add attention mechanism for better context awareness

Support for large-scale datasets

Web interface for real-time summarization

📜 License

This project is licensed under the MIT License.

💡 Inspiration

This project is inspired by the growing demand for automated content summarization in news aggregation, academic research, and digital marketing.