Condensing lengthy articles, research papers, and technical memos into actionable executive summaries requires isolating primary topic sentences while discarding repetitive filler. This client-side utility uses natural language frequency scoring to generate condensed summaries without relying on external cloud AI APIs.

### Extractive Summarization Mechanics The algorithm evaluates passage structure through deterministic linguistic parsing: - **Sentence & Token Segmentation:** Splits text on punctuation boundaries (`[.?!]`) into individual sentences and tokenizes words while filtering common English stop words. - **Term Frequency Scoring:** Calculates keyword importance based on document frequency. Words appearing multiple times across the passage receive higher semantic weight. - **Length Normalization & Positional Bias:** Normalizes raw sentence scores by square-root token count to prevent run-on sentence bias. Applies a 1.5× positional multiplier to paragraph opening sentences and 1.2× to second sentences to favor primary topic statements. - **Configurable Compression Ratio:** A range slider tunes target output length from 10% to 80% of original sentence count (default: 35%). - **Format Toggle:** Presents the output as either a cohesive running paragraph or an itemized bulleted list.

### Worked Summarization Scenario Using a 3-paragraph technical overview on frontend web frameworks (172 words, 7 sentences) at a 35% compression setting: 1. The engine calculates target sentence count: `ceil(7 × 0.35) = 3 sentences`. 2. The top 3 ranked topic sentences are selected and re-sorted to match their original chronological reading order. 3. Output length reduces from 172 words to 68 words (a 60% text reduction). 4. Results render instantly in either paragraph mode or bullet point mode with one-click clipboard copying.

### Client-Side Execution Notice All sentence segmentation, word frequency tallying, and summary text generation execute entirely in local browser memory. Sensitive drafts, internal documents, and academic manuscripts are never uploaded to remote servers.