https://github.com/nikhilchaudhary1/text-summarizer

A Python-based application for summarizing text using Extractive (TF-IDF) and Abstractive (T5 Transformer) techniques. Features an intuitive Streamlit UI for seamless interaction. Simply paste your text, choose a summarization type, and get concise summaries instantly!
https://github.com/nikhilchaudhary1/text-summarizer

abstractive-summarization extractive-summarization huggingface-transformers nltk python-nlp streamlit text-summarizer

Last synced: about 2 months ago
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A Python-based application for summarizing text using Extractive (TF-IDF) and Abstractive (T5 Transformer) techniques. Features an intuitive Streamlit UI for seamless interaction. Simply paste your text, choose a summarization type, and get concise summaries instantly!

• Host: GitHub
• URL: https://github.com/nikhilchaudhary1/text-summarizer
• Owner: NikhilChaudhary1
• Created: 2025-01-27T13:29:50.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-01-27T13:33:34.000Z (4 months ago)
• Last Synced: 2025-03-29T05:17:52.166Z (about 2 months ago)
• Topics: abstractive-summarization, extractive-summarization, huggingface-transformers, nltk, python-nlp, streamlit, text-summarizer
• Language: Python
• Homepage:
• Size: 3.91 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 📝 Text Summarizer

**A Python-based application implementing both Extractive and Abstractive text summarization techniques.**  

Simplify your long texts with an intuitive and user-friendly interface built using **Streamlit**.

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## 📜 Overview

The **Text Summarizer** application uses advanced natural language processing techniques to summarize large chunks of text into concise and meaningful content. Users can select between:  

- **Extractive Summarization**: Extracts key sentences from the input text using the **TF-IDF algorithm**.  

- **Abstractive Summarization**: Generates human-like summaries using HuggingFace’s **T5 Transformer model**.

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## 🚀 Features

- 🔄 **Dual Summarization Modes**:  

  - **Extractive**: Highlights the most important sentences from the text.  

  - **Abstractive**: Creates entirely new sentences to summarize the content.  

- 💻 **Streamlit-based UI**: A clean, interactive interface for inputting and summarizing text.  

- 🖱️ **Easy-to-Use**: Simply paste your text, select the summarization type, and get the summary at the click of a button.

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## 🛠️ Tech Stack

- **Programming Language**: Python 🐍  

- **Libraries and Tools**:  

  - `nltk`: Tokenization and stopword removal.  

  - `transformers`: HuggingFace's T5 model for abstractive summarization.  

  - `streamlit`: Intuitive UI for user interaction.  

- **Algorithms**:  

  - **TF-IDF**: For extractive summarization.  

  - **HuggingFace's T5-small Transformer**: For abstractive summarization.

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## 🧠 How It Works

1. **Extractive Summarization**  

   - Tokenizes the text and computes word frequencies, ignoring stopwords and punctuation.  

   - Scores sentences based on the word frequencies.  

   - Selects the top sentences to generate a summary.

2. **Abstractive Summarization**  

   - Uses the HuggingFace **T5-small Transformer** model to understand and generate a concise version of the input text.  

   - Produces summaries that feel natural and coherent.