Text Summarizer

Text Summarization using Deep Learning

This project focuses on automatically generating concise and meaningful summaries from long pieces of text using Natural Language Processing (NLP) and Deep Learning.

What the Project Does

The goal of this project is to take a large piece of text (such as an article, blog, or report) and generate a short, human-like summary that captures the main idea. This has applications in:

News summarization

Document analysis

Research paper review automation

Productivity tools

️ Steps I Followed to Build This Project

1️⃣ Problem Understanding & Dataset Preparation

Defined the task as sequence-to-sequence learning (long text → short text).

Collected and preprocessed a dataset of articles and summaries.

Applied tokenization, text cleaning, and handled variable sequence lengths through padding.

2️⃣ Model Selection & Architecture

Initially experimented with LSTM encoder-decoder models.

Later improved performance by adopting T5-small, a Transformer-based model from Hugging Face.

Integrated attention mechanisms to capture the most relevant parts of the text.

3️⃣ Training & Optimization

Used PyTorch and Hugging Face Transformers.

Implemented custom training loops with early stopping, learning rate scheduling, and gradient clipping.

Evaluated the model using metrics like ROUGE scores to assess summary quality.

4️⃣ Deployment & Usability

Designed a modern GUI where a user can input text and instantly get a summarized version.

This makes the project user-friendly and practical, not just a research experiment.