Text Summarizer

Purpose and usage are framed around reducing cognitive load when consuming long texts. The tool accepts a body of text, applies either extractive or abstractive summarization, and returns a concise result in the requested language and length. It is suitable for summarizing research papers, code READMEs, articles, or policy documents to support quick reviews and decision making. Conceptually, it normalizes input, computes sentence-level importance scores for extractive methods, or generates compact rewrites for abstractive methods, and then enforces a user-defined length constraint to produce the final summary. The system differentiates content by preserving key entities and central arguments while omitting incidental details. Use cases include literature surveys, briefing documents, and editorial preparation. The tool differentiates itself with explicit length control, language-aware processing, and optional multi-document aggregation, enabling consistent summaries across sources and domains. Core features include method selection (extractive vs abstractive), length controls (word count or percentage), language detection, and optional multi-document aggregation. Advanced users may bias outputs with keyword seeds and domain-specific models; results can be exported to downstream editors or knowledge bases. Typical workflows involve pasting or uploading text, selecting method and target length, running the summarization, and then reviewing or iterating as needed. This approach yields time savings, consistent summarization quality, and improved onboarding for new team members consuming external materials.