Text Summarizer

The tool condenses lengthy documents into concise outputs with configurable length and language support. It uses a two-stage approach: extractive sentence ranking followed by optional abstractive refinement to improve fluency while retaining accuracy. This is particularly valuable for researchers, students, and content teams who must distill reports, articles, or transcripts without sacrificing key entities. The MEAN stack architecture enables scalable API endpoints (Node.js/Express), robust data storage (MongoDB), and a responsive Angular-based client, all while keeping the summarization logic modular and separable from presentation. Core differentiators include length-controlled summaries, entity preservation, and optional refinement that improves readability without introducing misleading content. Typical use cases include briefing a meeting, creating study notes, or generating executive summaries for large literature reviews. The tool emphasizes data handling policies, supports multi-language input, and can integrate with downstream workflows via plain text exports and API responses.