What is the Text Summarizer?
This text summarizer selects the most important sentences that already exist in your text — it does not write new sentences. It uses a TextRank-style algorithm: every sentence is compared to every other sentence for shared vocabulary, that overlap forms a similarity graph, and a small PageRank-style calculation ranks each sentence by how central it is to the whole document. The highest-ranked sentences are pulled out, put back in their original order, and returned as the summary. Because the output is built entirely from your own sentences, it can never misquote or misrepresent what the source says — but it also cannot smooth two sentences into one or rephrase anything, since it only selects and reorders, it never generates.
This distinction — extractive versus abstractive summarization — matters more than it sounds. An abstractive summarizer (the kind a large language model produces) writes brand-new sentences that paraphrase the source, which reads more smoothly but can subtly drift from what the original actually said. An extractive summarizer like this one guarantees every sentence in the output is a verbatim sentence from your input, so you can always trace a claim in the summary back to its exact source sentence. That makes it a better fit for anything where accuracy to the source matters more than polish — meeting notes, research excerpts, legal or policy text.
Why use a free text summarizer?
Selects, never invents
Every sentence in the summary is copied verbatim from your source text — nothing is paraphrased, reworded or generated, so it cannot introduce a claim the original did not make.
Adjustable summary length
A slider sets the target compression from roughly 10% to 70% of the original sentence count, so you can go from a two-line gist to a lightly trimmed version.
See exactly which sentences were kept
The full original text is shown with selected sentences highlighted in place, so you can see what the algorithm judged central and what it left out.
Works on any topic
Because ranking is based on how much each sentence overlaps with the rest of the document rather than a fixed keyword list, it works the same way on a news article, a report or a set of meeting notes.
Nothing uploaded
The similarity graph and ranking run in your browser. Your text is never sent to a model or a server.
How do you use the Text Summarizer?
- 1
Paste your text
Works best on structured prose with several sentences — a single sentence or two has nothing to rank against, so the tool returns it unchanged.
- 2
Set your target length
Move the summary-length slider to choose roughly what percentage of the original sentences to keep. Lower percentages produce a tighter, more selective summary.
- 3
Read the summary
The selected sentences are shown in their original order, exactly as written — no wording is changed.
- 4
Check the highlighted original
Scroll to the highlighted view of your full text to see, in context, which sentences were kept and which were considered less central.
How is text summarizer calculated?
Sentence score = (1 − d) ÷ N + d × Σ [similarity(i,j) ÷ Σ similarity(j,*)] × score(j)Where:
- similarity(i,j)
- Cosine similarity between sentence i and sentence j, based on shared word frequency (stop words removed)
- d
- Damping factor, fixed at 0.85 — the same constant used in the original PageRank algorithm
- N
- Total number of sentences in the document
- score(j)
- The current rank score of sentence j, updated over 30 iterations until scores stabilise
The simple version
Every sentence gets compared to every other sentence for word overlap. A sentence that echoes the themes of many other sentences — the kind of sentence you would call "central" to the piece — earns a higher score, the same way a web page earns a higher PageRank score by being linked to by many other important pages. The highest-scoring sentences, up to your chosen length, become the summary.
What does it look like in practice?
A four-sentence paragraph summarized to 50%
Input
"Extractive summarization does not write new sentences. Instead it picks the most representative sentences already in the document. The technique is TextRank: sentences are compared for overlap, and a PageRank-style score ranks them. Sentences that echo the main themes score highly." (compression: 50%, target 2 of 4 sentences)
Result
"Extractive summarization does not write new sentences. Sentences that echo the main themes score highly."
The two middle sentences describing the mechanism were dropped in favour of the opening definition and the closing takeaway — the two sentences with the broadest overlap with the rest of the paragraph.
A single short sentence
Input
"The meeting is rescheduled to Friday."
Result
"The meeting is rescheduled to Friday."
With fewer than three sentences, there is nothing meaningful to rank against, so the summarizer returns the input unchanged rather than forcing an artificial cut.
How accurate is it?
The ranking is a direct, deterministic calculation — cosine similarity between every sentence pair, followed by 30 iterations of a PageRank-style power calculation with a damping factor of 0.85. Given the same text and the same target length, the output is identical every time; there is no randomness and no external model.
- Similarity is based on shared vocabulary (stop words like "the", "and", "is" are excluded), so two sentences that make the same point in completely different words will not be scored as similar — this is a lexical overlap measure, not a meaning-based one.
- The first sentence gets a small scoring boost, since opening sentences are disproportionately likely to be topic sentences in typical prose — this is a heuristic tuning choice, not a universal rule, and can occasionally over-favour a weak opening line.
- Very short documents (two sentences or fewer) are returned unchanged, since there is not enough content to build a meaningful similarity graph.
- "Compression" in the stats refers to fewer sentences, not necessarily proportionally fewer words — a summary can keep two very long sentences and still show a high percentage reduction in sentence count.
What should you know before using it?
What this tool is, where it runs and what it costs — the facts people usually check before trusting a free tool with their work.
| Detail | Text Summarizer |
|---|---|
| Category | Writing ToolsOne of 12 tools in this category. |
| Where it runs | Entirely in your browserYour input is never transmitted. Verify it yourself in the Network tab of your browser DevTools. |
| Price | FreeNo account, no trial, no usage cap, no paid tier. |
| Works offline | Yes, once the page has loaded |
| Also known as | free text summarizer, text summarizer online, online text summarizerOther names people search for this by. |
Which tools relate to the text summarizer?
The text summarizer is one of 12 writing tools on this site. These are the ones most often used alongside it — either because they handle the next step in the same job, or because they answer a question this tool raises.
Readability Score Checker
Run the summary itself through a readability check to confirm the shortened version reads at least as clearly as the source.
Word Counter
Check exact word counts for both the original text and the generated summary to see the real compression, not just the sentence-count percentage.
Reading Time Calculator
See how much reading time a summary actually saves compared to the full source text.
Paragraph Rewriter
If a summary sentence reads awkwardly out of its original context, use this tool to offer optional wording adjustments — the summarizer itself never changes a word.
When should you use a text summarizer?
Because the text summarizer runs entirely in your browser, it suits work you would not want to hand to a third-party server — client files, unpublished drafts, anything under an NDA. These are the situations people reach for it in most often.
Skimming long articles or reports
Paste a long report to get its most central sentences before deciding whether to read the whole thing.
Meeting notes and transcripts
Trim a lightly-edited transcript down to the sentences that actually carry decisions or action items, without risking a paraphrase that changes what was agreed.
Research and literature review
Extract the central claims from an abstract or a results section as verbatim sentences you can quote directly and attribute accurately.
Executive summaries from verbatim source text
Build a first-pass summary that is guaranteed to be traceable back to exact source sentences, then edit from there — safer than starting from a paraphrase in contexts where precision matters.
Frequently asked questions
Does this tool use AI to write the summary?
What is the difference between extractive and abstractive summarization?
Can I control how long the summary is?
Why did it summarize a short paragraph without changing anything?
Why did it pick that particular sentence instead of one that seems more important to me?
Is my text uploaded to a summarization model?
Can I use the summary as a direct quote?
Is the text summarizer safe to use?
This tool runs entirely inside your browser. Your text, files and settings are processed on your own device and are never uploaded to our servers — there is nothing for us to store, log or leak. You can confirm it yourself: open your browser DevTools, switch to the Network tab, and use the tool. You will not see an upload request. It also means the tool keeps working if your connection drops mid-task.
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