Mitigating AI Hallucinations in Automated News Summaries
AI News Aggregator with Smart Summarization is a modern, AI-driven news platform that consolidates articles from multiple trusted sources and generates concise summaries using advanced natural language models. It leverages the BART (facebook/bart-large-cnn) model to produce factual, context-preserving summaries while incorporating mechanisms to prevent hallucination or misinformation.
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The Solution
A Flask & React Dashboard with Entity Validation & Sentiment Scoring
The system features multi-source aggregation, real-time updates, and sentiment analysis for each article. Users can filter news by category, search specific topics, and switch between dark and light themes for a modern reading experience. It ensures that summaries remain faithful to original content through entity validation and extractive fallback logic.
Built with React 18 + TypeScript and Tailwind CSS on the frontend, and a Flask (Python) backend handling AI processing, the project combines efficient data fetching with deep NLP integration. The backend also uses BeautifulSoup and Newspaper3k for parsing and extracting article content, while the Transformers library powers intelligent summarization and hallucination prevention.
Comprehensive tests ensure reliability across diverse article types, achieving accurate summarization, entity preservation, and performance optimization. The platform demonstrates practical application of AI in journalism and real-time information systems.
