Text Summarizer — Free Online Text Summary Generator
Instantly condense long articles, essays, and documents into concise summaries. Control summary length with a word count slider. 100% client-side — your text stays private.
What is text summarization? Extractive summarization shortens a document by selecting the sentences it judges most important and returning them word for word, rather than writing new prose. This tool scores every sentence by the average frequency of the words it contains, keeps the highest scorers until your target word count is used up, then restores them to their original order so the summary still reads in sequence.
How to Use the Text Summarizer
- Paste the full text — Drop in the article, transcript or report. The scoring needs material to compare, so a long document summarizes far better than a handful of sentences, which will come back close to unchanged.
- Set the target length — The slider runs from 50 to 500 words in steps of 10 and starts at 150. Treat it as a budget rather than an exact figure — sentences are taken whole, so the result usually lands a little under the number you pick.
- Click Summarize — Scoring and selection happen in the page. There is no request to wait on, and the work scales with the number of sentences rather than the size of the file, so long documents return immediately.
- Check the ratio under the output — The note beside the Copy button reports the summary's word count and what percentage of the original it represents — the quickest way to see whether your target is genuinely condensing the text or handing most of it back.
- Copy and then read it through — Copy puts the summary on your clipboard. Always read it first: sentences are lifted out of context, so a pronoun or a connective such as "However" can end up without the sentence it referred to.
How the Summarizer Works
The document is first split into sentences by looking for a full stop, question mark or exclamation mark followed by whitespace. Every word of three letters or more is then counted across the whole text to build a frequency table — the working assumption of frequency-based summarization is that words an author repeats are the words the document is actually about. Each sentence is scored as the average frequency of its words:
score(sentence) = Σ frequency(word) / number of words in the sentenceDividing by the sentence length is what stops the longest sentence in the document from automatically winning, since a raw sum would reward length rather than density. Sentences are then sorted by score, and the highest are taken one at a time until adding the next would exceed your word budget. Finally the chosen sentences are put back into the order they appeared in the source, so the summary reads as a passage rather than a ranked list.
The whole method is unsupervised and deterministic: there is no model, no training data and no randomness, so the same text with the same slider position always produces the same summary. It is a close relative of the classic frequency-based approach described by Hans Peter Luhn in the 1950s, which remains the simplest workable way to rank sentences without any understanding of what they mean.
Extractive and Abstractive Summaries
There are two families of automatic summarization, and knowing which one you are using sets the right expectations for the output.
| Extractive (this tool) | Abstractive (language models) | |
|---|---|---|
| Method | Selects existing sentences and reproduces them verbatim | Generates new sentences that paraphrase the source |
| Wording | Always the author's own; quotable without checking | Rewritten, so wording must be verified against the source |
| Invented content | Impossible — nothing is written that was not there | Possible; claims can appear that the source never made |
| Compression | Limited by sentence boundaries; cannot merge two ideas | Can fuse several paragraphs into one sentence |
| Reading flow | Can feel abrupt where context was dropped | Usually smooth and connected |
| Where it runs | Entirely in this page, offline once loaded | Needs a hosted model and sends your text to it |
The trade-off is straightforward. An extractive summary cannot misrepresent the source by inventing a claim, which makes it the safer option for confidential documents and for anything you intend to quote. What it gives up is fluency: it can only ever return sentences that already exist, so where an author spread one idea across three paragraphs, this tool must either take all three or none.
Choosing a Summary Length
The right target depends on how long the source is and what you intend to do with the result. These are starting points rather than rules — check the percentage readout and adjust.
| Target | Roughly | Suits |
|---|---|---|
| 50 – 100 words | Two to four sentences | Deciding whether a document is worth reading at all |
| 100 – 200 words | A short paragraph | A news story, a blog post, an email thread |
| 200 – 350 words | Two or three paragraphs | A report or a meeting transcript where detail matters |
| 350 – 500 words | A page | Long papers, or when the summary must stand on its own |
Where the Method Falls Short
Frequency scoring has no concept of meaning, and its blind spots are predictable enough to work around. There is no stopword list beyond a length filter that ignores words of one or two letters, so common function words still contribute to the frequency table and can lift a sentence that says very little. Because the score is an average, a short sentence built from frequent words may outrank a longer one carrying the document's actual conclusion.
Sentence detection is punctuation-based, so abbreviations that end in a full stop followed by a space — Dr. Chen, e.g., U.S. Army — get split in the wrong place, and the fragments are scored as though they were sentences. Decimal numbers are safe, since no space follows the point. Word matching covers the basic Latin alphabet and the apostrophe, so accented and non-Latin scripts contribute nothing to the frequency table; on such text every sentence scores zero and the tool effectively returns the opening of the document.
The practical consequence is that the output is a reading aid, not a substitute for reading. It works best on well-structured expository prose in English — articles, reports, documentation — and worst on dialogue, poetry, lists and anything where the important sentence is deliberately understated.
Frequently Asked Questions
It is extractive and frequency-based. The text is split into sentences, every word of three or more letters is counted across the document, and each sentence is scored as the sum of its word frequencies divided by its length — an average rather than a total, so long sentences are not favored automatically. The highest-scoring sentences are collected until your word budget is spent, then put back into their original order so the summary reads in sequence.
No. There is no language model involved. This tool only selects sentences that already exist and returns them unchanged, which means it can never invent a fact or misstate a claim — a real advantage when the source is confidential or you intend to quote it. The trade-off is that it cannot rephrase, merge two related points into one sentence, or smooth over the gaps left where a sentence was skipped.
No. Splitting, counting and scoring all run in JavaScript in your own tab, so the document never leaves your device and the tool keeps working if you go offline after the page loads. One thing to note: your input and the slider position are written into the page URL so a result can be bookmarked or shared, so do not pass that link on if the text is sensitive.
Around 50–100 words is enough to decide whether a document is worth reading. 100–200 suits a news story or an email thread, 200–350 a report or meeting transcript where the detail matters, and 350–500 a long paper or a summary that has to stand on its own. The slider is a budget rather than a promise: sentences are added whole, so the result usually finishes slightly under your target.
Because whole sentences are lifted out of their surroundings. A sentence beginning "However, this meant…" is perfectly clear in place and confusing on its own once the sentence it contradicted has been dropped. Pronouns suffer the same way — "they" no longer has an antecedent. Raising the target length usually pulls in the neighboring sentences that supply the missing context.
Only partly. Words are matched with the pattern [a-z']+ after lowercasing, which covers the basic Latin alphabet and the apostrophe but nothing else. Accented letters break words apart and non-Latin scripts — Cyrillic, Greek, Arabic, Chinese, Japanese — produce no matches at all, so every sentence scores zero and the tool falls back to returning the document in order. It is reliable on English and on unaccented Latin-script text.
Sentence boundaries are found by looking for a full stop, question mark or exclamation mark followed by whitespace. That rule cannot tell a sentence end from an abbreviation, so Dr. Chen, e.g. and U.S. Army each get split into fragments, and those fragments are then scored as if they were sentences. Decimal numbers such as 3.14 are unaffected, since no space follows the point.
Not really. The only filter is on length: words of one or two letters are excluded from the frequency table, which removes a, an, of, to and is, but keeps the, and and that. There is no stopword list, so those frequent function words do influence the scores. Combined with the averaging, that occasionally lets a short, unremarkable sentence outrank a longer one carrying the real conclusion.
Use Cases
Triaging a Stack of Papers
Facing forty search results for a literature review, a 75-word summary of each is enough to decide which five deserve a full read — without uploading unpublished work anywhere.
Writing a Newsletter Blurb
Condense a long article to about 100 words, then edit that down by hand. Starting from the author's own sentences avoids the paraphrasing errors that creep into a rushed summary.
Catching Up on a Meeting Transcript
An hour of auto-generated transcript runs to thousands of words. A 300-word pass surfaces the recurring topics so you know which section to scroll back to.
Drafting a Meta Description
Summarize your own draft to 50 words to see which sentences the frequency scoring considers central — a quick check that the page is about what you think it is about.