Text Summarizer

What it does

Paste a long article, blog post, research paper abstract, or any block of text, and get back the 3, 5, or 10 most important sentences from the original. The summary uses extractive summarization — meaning it picks the actual sentences from your text that score highest by importance, rather than rewriting them. So the phrasing stays exactly as the original author wrote it (no risk of model "hallucination" or factual drift), and you can trust each sentence is a verbatim quote.

The ranking algorithm is a simple TF-IDF / TextRank hybrid: words that appear frequently in the document but are uncommon overall get higher weight, sentences containing high-weight words rise to the top, and the top N sentences (in original document order) become the summary. It's the same approach as classic search-engine snippet generation — fast, deterministic, and runs in pure JavaScript with zero dependencies.

Best for: skimming long articles before deciding to read fully, generating teaser-text for newsletters from a draft post, summarizing meeting transcripts to find key points, or distilling research papers down to the conclusions. Less good for: short text (under ~500 words doesn't have enough sentences to rank meaningfully), narrative fiction (importance scoring assumes informational text), and dense technical writing where every sentence matters.

How to use it

1. Paste your article into the input box. Best results with 500+ words and clear sentence boundaries.
2. Pick your summary length: 3 sentences (TL;DR), 5 (executive summary), or 10 (long-form summary).
3. Read the output — sentences appear in their original document order, so the summary reads naturally.
4. Click Copy to put the summary on your clipboard.
5. If a sentence feels off, you can manually edit the summary box — output is fully editable.

How it works

The text is split into sentences (using a punctuation-based tokenizer that handles abbreviations like "Dr." and "etc."). Each sentence is then scored by:

1. TF-IDF: word frequency in this document divided by frequency across a baseline English corpus. Common words (the, a, of, and) score near zero; document-specific words (the topic's vocabulary) score high.
2. Sentence score = sum of word scores in that sentence, normalized by sentence length so longer sentences don't automatically win.
3. Top-N selection: the highest-scoring sentences are picked, then re-sorted into their original document order so the summary reads naturally.

No machine learning, no model download, no API call — just statistics on word counts. ~50 lines of JavaScript.

When to use this tool

• Skimming a long article before deciding whether to read it fully.
• Generating a teaser paragraph for a newsletter from a longer draft post.
• Distilling meeting transcripts down to actionable points.
• Summarizing research-paper sections (abstract → conclusions in 3 sentences).

When not to use it

• Anything where you need NEW phrasing (rewriting for tone, audience, or style) — this preserves the original wording. Use a generative LLM for paraphrasing.
• Short input (under ~500 words) — there aren't enough distinct sentences for the ranking to find meaningful highlights.
• Narrative fiction — extractive summarization assumes informational text where each sentence carries weight differently. In a story, plot beats matter more than word frequency.
• Highly technical / dense writing where every sentence is essential — extractive will cut what looks redundant but may have been load-bearing.