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.
- Paste the text you want to summarize into the input field.
- Select the number of sentences for the summary (typically 3–5 for most articles).
- Click "Summarize" to run the extractive sentence-scoring algorithm.
- Read the summary — every sentence is drawn directly and verbatim from the original text.
- 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
Frequently Asked Questions
Why use Text Summarizer?
- Condense long articles into key sentences ranked by content-word frequency and position
- Extractive method guarantees every summary sentence is verbatim from the original source
- Adjustable sentence count tailors summary length from a two-line abstract to a full brief
- Stop-word filtering focuses sentence scoring on semantically meaningful content terms
Common use cases
- Reduce a 2,000-word research paper to five key sentences for an executive summary
- Extract the most important facts from a long news article before sharing with a team
- Create concise study notes from a textbook chapter by selecting the top three sentences
- Curate a weekly reading digest by summarizing each source article to two sentences
- Absorb a dense industry report without reading the full document from start to finish
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