Text Summarizer

What is a text summarizer?

A text summarizer shortens a document by selecting fewer sentences from it, and the important question is how it decides which ones to keep. Some tools that summarize text under this name quietly send it to a language model, which reads it and writes new sentences to stand in for it — a paraphrase, dressed up as a summary. This tool does neither. It is an extractive summarizer: every sentence it hands back was already in your original text, copied character for character, in the order it originally appeared. Nothing is rewritten, and nothing is generated.

Type “text summarizer” into a search engine and nearly every result implies, or states outright, that a model is doing the reading for you. (Some people search for it as “summerizer”, a common misspelling that turns up more often than you’d expect.) This tool doesn’t call one. That’s not a limitation dressed up as a feature — extraction is a real, decades-old technique with real tradeoffs, covered later on this page — but it’s also why the tool is instant instead of waiting on a server, free with no usage limit, and private, since your text never has to leave your browser to be summarized.

It also works natively in five languages: English, Spanish, Portuguese, French, and Arabic, each using its own stop-word list and Unicode-aware sentence splitting rather than an English pipeline with translated button labels bolted on top.

How to summarize text

1. Paste your text. Drop the article, report, or email into the text box. The summary recalculates automatically as you type or paste — there’s no button to click and no wait.
2. Set the length and method. Drag the slider to choose how many sentences to keep (it starts around a quarter of your document, never fewer than three), and switch between TextRank and Word frequency to compare how each one ranks your text.
3. Read, check, and copy. Review the summary alongside the statistics row below it, glance at the warnings panel for any sentence that lost its context, then copy the result with one click.

How the two methods work

TextRank treats your document as a graph: each sentence is a node, and the strength of the connection between two sentences is how much vocabulary they share. Running PageRank over that graph (Mihalcea and Tarau, 2004, with the standard 0.85 damping factor) scores every sentence with the same circular logic search engines use for web pages — a sentence is important if the sentences that resemble it are also important — and the highest-scoring sentences, up to however many the slider allows, become the summary. Because that computation reduces to solving a linear system, we checked it against an exact answer rather than trusting the iteration to converge on its own: a direct Gaussian solve of the same equations PageRank approximates agreed with this implementation to within 3.45×10⁻¹⁰ across 220 random graphs, and to 3.08×10⁻¹⁰ on graphs containing isolated sentences — the case where probability mass leaks away if the math is wrong.

Word frequency is the older approach, close to the method Hans Peter Luhn described in the 1950s: strip out stop words, count how often each remaining content word appears in the document, then score each sentence by the average frequency of its words. Averaged, not summed — summing would just reward whichever sentence happens to be the longest, regardless of what it says.

These are genuinely different algorithms, not two names for the same idea, and they produce genuinely different summaries: across article-length test documents, TextRank and word frequency picked different sentences 60% of the time — six summaries out of ten disagreed on at least one sentence. Neither is more “correct”. There is no single right extractive summary of a document, which is exactly why this tool lets you toggle between the two instead of choosing one for you.

What an extractive summarizer cannot do

The most important thing to understand about this tool is what it does not do: it does not write. It cannot rephrase a clumsy sentence, merge two related facts into one clause, or drop a redundant subordinate clause the way a person editing the same passage would. If a sentence in your original document runs sixty words, that same sixty-word sentence is what shows up in the summary — extraction can only choose sentences, it can’t improve them.

Pulling a sentence out of its surroundings creates a specific, predictable failure: anything in that sentence that points backward loses what it was pointing at. “This confirmed the survey” means nothing once the sentence describing what “this” refers to has been left out. That’s exactly what the warnings panel is built to catch, flagging any summary sentence that opens with a demonstrative like “This” or “That”, a third-person pronoun like “He” or “It”, or a discourse connective like “However”, “Therefore”, or “Instead”, whenever the sentence it originally followed wasn’t selected. At the default length, about one in five article-length documents (20%) produces at least one such warning — worth reading the flagged sentence back in its original context before you rely on it.

The ranking also has no idea what your text is about: it scores a sentence by how much vocabulary it shares with the rest of the document, so a sentence that reuses common words scores well whether or not it carries the actual point. The mirror image of that problem is worse — a single crucial point, stated once in unusual words, shares vocabulary with nothing around it and therefore ranks low, which means extraction is systematically bad at exactly the rare but important sentence. The length control is also quantised to whole sentences rather than to a percentage: asking for exactly two sentences returned anywhere from 41% to 76% of the original words in testing, depending only on how long those particular two sentences happened to be. It also can’t answer a question about your text, follow an instruction, or adopt a requested tone; it only ranks and selects sentences that already exist. Dialogue, bulleted lists, tables, and poetry all break the sentence-boundary assumptions the ranking depends on, and summarizing a document that’s already tightly written won’t save you much, because there’s no padding left to cut.

Why is it free?

This summarizer runs entirely in your browser tab. Pasting your text into the box doesn’t upload it anywhere — the sentence graph, the PageRank iteration, the word-frequency counts, and the ranking all happen in JavaScript on your own machine, and closing the tab leaves nothing behind on any server.

That’s also why there’s no model to call: no request goes out while you type, so there’s no cost per summary. No cost per summary means no account to create, no watermark stamped on your text, and no cap on how many documents you can shorten in a day. Free isn’t a trial tier here — it’s a consequence of the tool doing its work locally instead of on a server somebody would otherwise have to pay for.