Summarizer JS

Purpose & User Intent: The tool solves the problem of turning long text into concise, insight-focused summaries. It is used by writers, researchers, students, editors, and developers who ingest articles, reports, notes, or documentation and need a quick, accurate understanding of core ideas. The goal is to reduce reading time while preserving essential context. Inputs are text blocks, and outputs are compact summaries suitable for outlines or decision-making. Core Logic & Features: Required features include a text input, a selectable summary length (short/medium/long), and a choice between extractive and abstractive approaches. Advanced options include language detection, multi-language support, and markdown/plain text handling. Optional features encompass export as plain text or JSON, and per-language tuning. The system emphasizes fidelity to key ideas, with safeguards against misrepresentation. Inputs & Outputs: Inputs consist of text (string, sample: a 1,000–2,000 word article), an optional target length, and a mode (extractive/abstractive). Outputs include a summary string and a summary length metric (word count). Validation rejects empty input and applies chunking for very large texts, producing coherent sections. Algorithms & Calculations: The tool uses a dual-path approach. Extractive mode scores sentences by relevance using frequency cues and graph-based scoring; abstractive mode generates paraphrased summaries with a lightweight JS model. Processing is client-side to preserve privacy. Error & Edge Cases: Empty or non-text input yields no summary; extremely long inputs may be chunked into coherent sections; language not supported triggers English fallback or a language-not-supported notice; parsing errors return a structured empty result. Industry/Region & Localization: Primary support is English; language detection enables approximate handling of additional languages with graceful fallback. No currency or region-specific formatting is required; locales affect language but not core logic. Assumptions & Exclusions: Assumes input is coherent natural language text; does not translate or perform sentiment analysis by default; not a replacement for human review; does not process multimedia files directly (images, PDFs) unless text is extracted first.