https://github.com/snigdho8869/text-summarization-with-transformers

A collection of text summarization projects using ensemble methods and T5 model.
https://github.com/snigdho8869/text-summarization-with-transformers

ai-projects ai-summarization bart-model css deep-learning ensemble-learning flask flask-application gpt-2 html html-css-javascript javascript natural-language-processing nlp python t5-model text-summarization text-summarization-with-transformers transformers web-development

Last synced: 3 months ago
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A collection of text summarization projects using ensemble methods and T5 model.

• Host: GitHub
• URL: https://github.com/snigdho8869/text-summarization-with-transformers
• Owner: Snigdho8869
• Created: 2025-03-16T18:27:08.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-16T19:35:26.000Z (3 months ago)
• Last Synced: 2025-03-16T19:44:42.205Z (3 months ago)
• Topics: ai-projects, ai-summarization, bart-model, css, deep-learning, ensemble-learning, flask, flask-application, gpt-2, html, html-css-javascript, javascript, natural-language-processing, nlp, python, t5-model, text-summarization, text-summarization-with-transformers, transformers, web-development
• Language: Jupyter Notebook
• Homepage:
• Size: 126 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Text Summarization Projects

This repository contains two text summarization projects built using natural language processing (NLP) models. The projects are implemented using Flask for the backend and HTML/CSS/JavaScript for the frontend.

---

## Projects Overview

### 1. **Ensemble Summarization**

This project uses an ensemble of two pre-trained models, **BART** and **GPT-2**, to generate summaries of input text. The ensemble method combines the outputs of both models and selects the most frequent summary as the final result.

- **Models Used**:

  - **BART (Bidirectional and Auto-Regressive Transformers)**: A transformer-based model fine-tuned for summarization tasks.

  - **GPT-2 (Generative Pre-trained Transformer 2)**: A transformer-based model capable of generating coherent and contextually relevant text.

- **Features**:

  - Input text is summarized using both models.

  - The final summary is selected based on the most frequent output.

  - Interactive web interface for input and output.

---

### 2. **T5 Summarization**

This project uses the **T5 (Text-To-Text Transfer Transformer)** model for text summarization. T5 is a versatile transformer model that treats all NLP tasks as a text-to-text problem, making it highly effective for summarization.

- **Model Used**:

  - **T5 (Text-To-Text Transfer Transformer)**: A transformer-based model fine-tuned for summarization tasks.

- **Features**:

  - Input text is summarized using the T5 model.

  - Interactive web interface for input and output.

---

## Technologies Used

- **Backend**:

  - Python

  - Flask (Web framework)

  - Hugging Face Transformers (for pre-trained models)

  - PyTorch (for model inference)

- **Frontend**:

  - HTML

  - CSS

  - JavaScript

- **Pre-trained Models**:

  - BART (`facebook/bart-large-cnn`)

  - GPT-2 (`gpt2`)

  - T5 (`t5-base`)

---

## Application Interface

**T5 Summarization**






**Ensemble Summarization**