https://github.com/arpan132002/abstractive-text-summarization-transformer-model-nlp

This project implements an abstractive text summarization model using transformer architecture.
https://github.com/arpan132002/abstractive-text-summarization-transformer-model-nlp

deep-learning mini-projects nlp transformer

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This project implements an abstractive text summarization model using transformer architecture.

• Host: GitHub
• URL: https://github.com/arpan132002/abstractive-text-summarization-transformer-model-nlp
• Owner: arpan132002
• Created: 2024-08-05T11:17:58.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-05T12:32:47.000Z (6 months ago)
• Last Synced: 2024-08-05T14:24:53.712Z (6 months ago)
• Topics: deep-learning, mini-projects, nlp, transformer
• Language: Jupyter Notebook
• Homepage:
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Abstractive Text Summarization Transformer Model

## Overview

This project implements an abstractive text summarization model using transformer architecture. Abstractive text summarization aims to generate a concise and coherent summary of a longer text document by understanding and rephrasing the content, rather than just extracting key sentences.

## Features

- **Transformer Architecture**: Utilizes state-of-the-art transformer models for text summarization.

- **Abstractive Summarization**: Generates summaries that are not just copied from the text but are newly constructed sentences.

- **Customizable and Scalable**: Easy to adapt and fine-tune for different datasets and applications.

- **Support for Multiple Languages**: Can be extended to support various languages with appropriate training data.

## Usage

Prepare Data: Ensure your dataset is in the appropriate format.

Train Model: Train the transformer model using your dataset.

Generate Summaries: Use the trained model to generate summaries for new text documents.

## Example

```python

from model import Summarizer

# Initialize the summarizer

summarizer = Summarizer()

# Load pre-trained model or train a new one

summarizer.load_model('path/to/model')

# Summarize text

text = "Your long text document here..."

summary = summarizer.summarize(text)

print("Summary:", summary)