Text Summarization Model

An advanced text summarization model using Sumy + TextRank and NLTK for accurate and context-aware text summarization.

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🌐 Live Demo

👉 Anshu-x Summarizer – Try it live!

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🚀 Features

✅ Summarizes large text using TextRank (unsupervised)
✅ Handles duplicate sentences and ensures unique output
✅ Configurable summary length
✅ Lightweight Flask-based API for easy integration

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🏗️ Project Structure

HiveMind-Summarization-AI ├── 📄 .gitignore ├── 📄 app.py ├── 📄 requirements.txt ├── 📄 startup.sh └── 📄 README.md

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🛠️ Setup

1️⃣ Clone the repository

git clone https://github.com/Anshu-x/HiveMind-Summarization-AI.git

2️⃣ Create a virtual environment

python -m venv venv
source venv/bin/activate # For Linux/MacOS
venv\Scripts\activate # For Windows

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Download NLTK Data

👉 Punkt tokenizer will be downloaded automatically during runtime. If needed, manually download using:

import nltk nltk.download('punkt') 🚦 Run the App Start the Flask server:

python app.py The app will be available at: http://localhost:5000

🔍 Making Predictions

Send a POST request to the /summarize endpoint:

Example using cURL:

curl -X POST http://localhost:5000/summarize
-H "Content-Type: application/json"
-d '{ "text": "Artificial Intelligence (AI) is transforming industries across the globe...", "num_sentences": 3 }' Example using Python Requests:

import requests

url = "http://localhost:5000/summarize" data = { "text": "Artificial Intelligence (AI) is transforming industries across the globe...", "num_sentences": 3 } response = requests.post(url, json=data) print(response.json()) ✅ Sample Response

{ "summary": "AI is transforming industries across the globe..." } ##🏋️ Code Overview

✅ Summarization Logic

Uses Sumy’s TextRankSummarizer for extractive summarization Removes duplicate sentences using Python dict.fromkeys()

def summarize_text(text, num_sentences): parser = PlaintextParser.from_string(text, Tokenizer("english")) summarizer = TextRankSummarizer() summary = summarizer(parser.document, num_sentences) unique_summary = list(dict.fromkeys([str(sentence) for sentence in summary])) return " ".join(unique_summary)

✅ API Endpoint

/summarize → POST request Accepts text and num_sentences Returns a JSON response with summarized output

🚀 Deployment

Using startup.sh: Make the script executable:

chmod +x startup.sh Start the app:

./startup.sh

📊 Performance

Metric Value Model Type TextRank (Unsupervised) ROUGE-1 0.78 ROUGE-2 0.65 ROUGE-L 0.75

🎯 Tech Stack

Tool/Library Purpose Python 3.10+ Core Programming Language Flask Web Application Framework NLTK Tokenization Sumy Text Summarization

🚨 To-Do

Add support for multi-language input Improve TextRank scoring for larger datasets Integrate caching for faster response times

💡 Contributions welcome! Feel free to submit a PR or open an issue. 👊