Skip to content

README.md

Latest commit

 

History

History
152 lines (107 loc) · 3.15 KB

README.md

File metadata and controls

152 lines (107 loc) · 3.15 KB

📝 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.

🏗️ 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

⚙️ Tech Stack

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

📂 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

🔄 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

🚀 Key Features

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

▶️ 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

📌 Future Enhancements

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

👨‍💻 Author

Naman Singhal

⭐ Acknowledgements

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