📝 Text Summarizer – NLP Project

📌 Overview

This project is a Text Summarization system built using Python and Natural Language Processing (NLP) techniques. It processes raw text data and generates concise summaries while preserving key information.

The project demonstrates a complete pipeline including data ingestion, preprocessing, transformation, and summarization.

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🏗️ Architecture

🔷 High-Level Architecture

        Raw Text Data (Files / Input)
                    │
                    ▼
           Data Ingestion Layer
                    │
                    ▼
        Data Preprocessing Layer
   (Cleaning, Tokenization, Stopwords)
                    │
                    ▼
        Data Transformation Layer
                    │
                    ▼
          Summarization Model
                    │
                    ▼
            Final Summary Output

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⚙️ Tech Stack

• Python
• NLP (Natural Language Processing)
• NLTK
• Pandas
• Docker

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📂 Project Structure

TextSummarizer/
│
├── artifacts/
│   ├── data_ingestion/                # Raw data storage
│   ├── data_transformation/           # Processed datasets
│
├── config/                            # Configuration files
├── logs/                              # Application logs
├── research/                          # Experimentation notebooks
├── src/                               # Core source code
│
├── app.py                             # Application entry point
├── main.py                            # Pipeline execution script
├── Dockerfile                         # Container setup
├── README.md

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🔄 Pipeline Flow

1️⃣ Data Ingestion

• Loads raw text data from input sources
• Stores data in artifacts directory

2️⃣ Data Preprocessing

• Text cleaning (removing punctuation, special characters)
• Tokenization
• Stopword removal

3️⃣ Data Transformation

• Feature extraction
• Text normalization
• Preparation for model input

4️⃣ Summarization

• Generates summary using NLP techniques
• Extractive or abstractive approach

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🚀 Key Features

• Modular pipeline design
• Reusable components
• Logging and configuration support
• Dockerized for easy deployment

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▶️ How to Run

🔹 Local Setup

1. Clone the repository

2. Install dependencies:

   pip install -r requirements.txt

3. Run the pipeline:

   python main.py

🔹 Using Docker

1. Build image:

   docker build -t text-summarizer .

2. Run container:

   docker run text-summarizer

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📌 Future Enhancements

• Add transformer-based models (BERT, T5)
• API deployment (FastAPI/Flask)
• UI for user interaction
• Real-time summarization

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👨‍💻 Author

Naman Singhal

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⭐ Acknowledgements

This project is built for learning and demonstrating NLP-based text summarization pipelines.