Skip to main content
ToolsHub

Text Summarizer

Paste an article and get a short summary of its most important sentences, ranked by keyword frequency. Pick how many sentences to keep — all processed locally, nothing uploaded.

Updated

Files never leave your browser

No summary yet

Paste an article or long passage on the left to generate a short summary.

How to use Text Summarizer

Condense any long article, essay, or document into its most important sentences using extractive summarization — processed entirely in your browser with no text ever sent to a server. Paste your content, choose how many summary sentences you need, and instantly receive a shorter version built from the highest-scoring sentences in the original text. Ideal for research, study notes, executive briefings, content curation, and quickly absorbing long-form sources without reading every word.

  1. Paste the text you want to summarize into the input field.
  2. Select the number of sentences for the summary (typically 3–5 for most articles).
  3. Click "Summarize" to run the extractive sentence-scoring algorithm.
  4. Read the summary — every sentence is drawn directly and verbatim from the original text.
  5. Adjust the sentence count and re-run if the result is too short or too long for your needs.

Your data never leaves your device — 100% private processing.

Extractive vs. abstractive summarization

Extractive summarization scores every sentence in the source text using keyword frequency, sentence position (opening and closing sentences score higher), and proximity to the document centroid. The highest-scoring sentences are assembled in their original order — no new words are generated; all output is drawn verbatim from the input. Abstractive summarization, by contrast, generates entirely new paraphrased sentences using large neural language models such as BART or T5. Abstractive models produce more fluent summaries but require substantial server-side compute that cannot run efficiently in a browser.

How sentence scoring works

The scorer assigns weight to content words based on their frequency in the document, filtering out common stop words (e.g., "the", "is", "and") that carry little semantic meaning. Each sentence's score is the sum of its content-word weights, normalized by sentence length to prevent long sentences from receiving an unfair advantage. Sentences in the first and final paragraphs receive a small positional bonus, reflecting the journalistic convention of placing key facts in the lead and conclusion. The N highest-scoring sentences are returned in their original document order.

Glossary

Extractive summarization
A summarization method that selects and assembles existing sentences from the source text without generating any new words or phrases.
Abstractive summarization
A summarization method that generates new paraphrased sentences using a language model, producing more fluent but computationally expensive summaries.
TF-IDF
Term Frequency–Inverse Document Frequency — a weighting scheme that scores words by how often they appear in a document relative to their frequency across a wider corpus.
Stop word
A high-frequency function word (e.g., "the", "is", "and") filtered out before text analysis because it carries little semantic meaning on its own.
Centroid
In text summarization, a pseudo-document representing the average semantic content of the source, used as a reference to score how representative each sentence is.

Related reading

Free · No spam

Get weekly tool tips & updates

New tools, power-user tips, and productivity hacks — delivered free every Friday.

No spam, ever. Unsubscribe with one click.

Related AI & Writing

Explore all AI & Writing.