Policy Simplifier NLP

An intelligent Policy Simplification System that converts complex policy or legal text into simple and easy-to-understand language using Natural Language Processing (NLP) and Machine Learning.

This project is designed to help users understand complicated policies from government, finance, or organizational documents by simplifying them into clear and readable text.

---

Project Overview

Policies and legal documents are often written in complex language that is difficult for the general public to understand.
This system uses NLP techniques and transformer-based models to simplify policy text automatically.

The system can:

• Accept complex policy text as input
• Analyze and process the text using NLP
• Simplify the sentences while keeping the meaning intact
• Display a clearer and easier version of the policy

---

Features

• Policy text simplification using NLP
• Transformer-based language model support
• Rule-based simplification techniques
• Grammar correction and readability improvement
• Web interface using Flask
• Supports long policy inputs

---

Technologies Used

• Python
• Flask
• Natural Language Processing (NLP)
• Transformers Library
• PyTorch
• NLTK
• HTML / CSS
• Machine Learning

---

Project Structure

Policy-Simplifier-NLP
│
├── app.py
├── model.py
├── test.py
├── requirements.txt
├── Indian_Policy_Texts.docx
│
├── instance
│ └── users.db
│
├── templates
│ ├── index.html
│ ├── login.html
│ ├── register.html
│ ├── forgot_password.html
│ └── reset_password.html
│
└── README.md

---

Installation

1. Clone the repository

git clone https://github.com/Adityakumar926/Policy-Simplifier-NLP.git

2. Navigate to the project directory

cd Policy-Simplifier-NLP

3. Install dependencies

pip install -r requirements.txt

---

Running the Project

Start the Flask server: python app.py

Then open your browser and go to: http://127.0.0.1:5000/

---

Example Use Case

Input (Complex Policy Text):

"Employees are required to adhere to organizational compliance protocols and ensure adherence to regulatory standards."

Simplified Output:

"Employees must follow company rules and meet required regulations."

---

Future Improvements

• Multi-language policy simplification
• Improved summarization techniques
• Better semantic understanding
• Integration with government policy datasets
• Advanced readability scoring

---

Author

Aditya Kumar

B.Tech – Computer Science Engineering
Machine Learning & NLP Enthusiast

GitHub:
https://github.com/Adityakumar926

---

License

This project is for educational and research purposes.