Sample Output

NewsQuest-NLP

A lightweight Natural Language Processing (NLP) system that automatically converts live news articles from RSS feeds into educational questions using rule-based language processing.

Overview

NewsQuest-NLP is a Python-based educational application that demonstrates how Natural Language Processing (NLP) techniques can be used to transform real-time news into learning material.

Instead of manually creating questions from news articles, the system automatically fetches the latest headlines from multiple RSS feeds, removes duplicate stories, analyzes article titles using rule-based NLP, and generates meaningful educational questions.

The project was designed as the first version of the NewsQuest ecosystem, focusing on simplicity, explainability, and lightweight execution without requiring large language models.

Features

• Fetches live news from multiple RSS feeds
• Automatically removes duplicate news articles
• Generates educational questions using rule-based NLP
• Lightweight and fast execution
• Modular project architecture
• Easily extendable with future AI models

Project Architecture

         RSS News Sources
                │
                ▼
       rss_fetcher.py
                │
    Fetch & Filter Articles
                │
                ▼
 nlp_question_generator.py
                │
  Rule-Based NLP Processing
                │
                ▼
      Generated Questions
                │
                ▼
           main.py

Project Structure

NewsQuest-NLP/ │ ├── main.py ├── rss_fetcher.py ├── nlp_question_generator.py ├── requirements.txt ├── README.md └── .gitignore

Technologies Used

• Python
• Feedparser
• Rule-Based Natural Language Processing (NLP)
• RSS Feeds

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Installation

1. Clone the repository:

bash git clone https://github.com/Rahulcj69/NewsQuest-NLP.git

2. Move into the project directory:

bash cd NewsQuest-NLP

3. Install dependencies:

bash pip install -r requirements.txt

4. Run the application:

bash python main.py

Workflow

1. Fetch news articles from multiple RSS feeds.
2. Remove duplicate news stories.
3. Extract article titles.
4. Analyze titles using rule-based NLP.
5. Generate educational questions.
6. Display generated questions to the user.

Example Output

Daily Questions

1. What were the key reasons behind this outcome?

Source: India wins second ODI against Australia.

2. What precautions should be taken in case of this event?

Source: Heavy rainfall expected across North India.

3. What could be the impact of this development?

Source: Government announces new education policy.

Motivation

The project was created to explore how Natural Language Processing can be applied to transform real-time information into educational content.

It demonstrates modular software design, RSS data processing, duplicate filtering, and rule-based question generation while serving as a foundation for future AI-powered versions.

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Future Improvements

• Integrate Gemma 4 for AI-generated questions
• Support multiple question types (MCQ, True/False, Short Answer)
• Difficulty-aware question generation
• Streamlit-based web interface
• Question categorization by topic
• Export generated questions to PDF and CSV
• Multi-language support

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Next Evolution

The next version of this project is NewsQuest-Gemma, which replaces rule-based NLP with a locally running Gemma 4 Large Language Model through Ollama to generate more context-aware and intelligent educational questions.

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Author

Rahul Mandyal

M.Tech Computer Science and Engineeringo

AI • Machine Learning • Generative AI • Python

GitHub: https://github.com/Rahulcj69