A Desktop Intelligent Content Aggregator with 
Topic-Aware Personalization and Lightweight 
AI Summarization

Umar Nasir Sayyed

A Desktop Intelligent Content Aggregator with Topic-Aware Personalization and Lightweight AI Summarization

Author(s)	Umar Nasir Sayyed
Country	India
Abstract	This project develops a desktop Intelligent Content Aggregator that reduces information overload by unifying articles from multiple RSS/Atom sources, extracting clean “reader-mode” content, and applying lightweight on-device AI (TextRank summaries, RAKE keywords, VADER sentiment). Built with Python + PySide6/QML and MySQL, the system supports multi-user login: each user defines topics during account creation, and the application filters the corpus so that only topic-relevant items are shown. A responsive master–detail UI presents a left-hand article list and a right-hand pane with Summary (AI-generated), Reader (clean HTML), and Web (live page) views. All network and processing tasks run in background threads to keep the interface fluid on commodity hardware. Methodologically, the pipeline ingests feeds, deduplicates by URL, fetches pages with robust timeouts and user-agent handling, and performs readability extraction to strip ads/trackers and boilerplate. The resulting text is enriched with extractive summaries, keyword sets, and sentiment scores and stored in a relational schema (sources, articles, users, user_topics, user_article_state) that enables per-user personalization and search (FULLTEXT with LIKE fallback). Evaluation focuses on triage speed (time-to decision: read/skip/save) and relevance (precision of shown items against user topics). We target a practical improvement of ≥20–30% reduction in median decision time versus a baseline feed reader with titles only, alongside positive user ratings for readability and usefulness. The anticipated outcome is a privacy-preserving, maintainable, and extensible desktop reader that surfaces only what matters to each user and enables faster decisions through concise AI assistance providing a strong foundation for future enhancements such as advanced summarizers, user-specific source sets, and cross-device sync.
Field	Computer Applications
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-07

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International Journal For Multidisciplinary Research

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A Desktop Intelligent Content Aggregator with Topic-Aware Personalization and Lightweight AI Summarization

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