Updated · Built and checked by the PlinzoTools team, formulas tested against published sources
Extract the most important sentences from any text using frequency-based extractive summarization.
Regular exercise improves cardiovascular health Many people find exercise boosts their mood
This tool picks out the most representative sentences from your text using classic frequency-based extractive summarization: each sentence is scored by how many high-frequency, significant words it contains, then the top-scoring sentences are returned in their original order. Every sentence in the summary is copied verbatim from your input -- nothing is generated or rewritten.
Paste your text and choose how many sentences you want in the summary. The extracted summary appears instantly.
1. Build a frequency count of every significant (non-filler) word.
2. Score each sentence by the average relative frequency of its words.
3. Select the top-N highest-scoring sentences, then reorder them to match their original position in the text.
This is extractive summarization (selecting existing sentences), not abstractive summarization (generating new sentences) -- it works best on longer, well-structured text with clear topic sentences.
The pre-filled text has five sentences about exercise, and the tool is set to return two. After removing filler words, the most frequent significant word is "exercise", which appears three times. Each sentence is scored by adding up the relative frequency of its words (a word's count divided by the highest count) and dividing by the number of words in that sentence. Working it through gives approximate scores of 0.47 for "Regular exercise improves cardiovascular health", 0.38 for "Many people find exercise boosts their mood", 0.29 for the hydration sentence, 0.21 for the muscles-and-bones sentence, and 0.20 for the routine sentence. The top two are picked and shown in their original order, so the summary reads: "Regular exercise improves cardiovascular health. Many people find exercise boosts their mood."
This tells you something about the algorithm's personality. It favours short, dense sentences that repeat the text's dominant words. A long, informative sentence with many different words scores lower because its average is diluted, even if it contains important facts.
You choose how many sentences to keep. If you enter a number larger than the number of sentences available, the tool simply returns all of them. If the box is empty or zero, the tool falls back to two sentences. Sentences are detected by looking for periods, exclamation marks or question marks followed by a space or the end of the text, so a decimal such as 4.5 stays intact, while an abbreviation like "Dr." followed by a space will split a sentence and may yield an odd fragment. Paragraph breaks are lost in the output, because the chosen sentences are joined with single spaces.
Because the method only counts words, it has no sense of meaning. It cannot recognise that a sentence with a rare but crucial term, such as a conclusion or a warning, is important. It also has no positional bias, so an introductory sentence is not favoured just because it comes first. Texts with a lot of variety, such as interviews or lists of unrelated items, produce summaries that feel random. Very short inputs of two or three sentences give you back nearly the whole text. Tables, code and bullet lists without punctuation may merge into very long pseudo-sentences that score oddly.
Clean the input first: remove navigation text, captions and repeated boilerplate, since these add words to the frequency table and pull the selection toward irrelevant sentences. Try several summary lengths; the top-ranked sentences stay the same as you increase the count, so raising the number only adds lower-scoring ones. Then read the extract critically. Because sentences are lifted verbatim, they may contain pronouns such as "it" or "this" that referred to something in a sentence that was dropped. Edit those by hand so the summary stands alone. Also keep in mind that if you publish the extracted sentences, they are still the original author's words and need proper attribution when they are not your own.
The same text always yields the same output, which makes the tool predictable and easy to audit. You can see exactly why a sentence was chosen: it contained the most repeated topic words. That transparency is useful when you need a repeatable process, for example when comparing several documents with the same settings, or when you cannot send private text to an outside service, since all the processing happens in your browser.
No -- it's a deterministic word-frequency algorithm. The same input always produces the same summary, and every sentence is your own original text.
The tool scores sentences by how many frequent topic words they contain, not by meaning. An important sentence that uses unusual vocabulary, or is long, will score lower than a short sentence packed with repeated terms.
It returns every sentence in the text in the original order, so the summary is just the full input.
No. Selected sentences are joined into one block separated by spaces, so you need to re-add paragraph breaks yourself if you want them.
No. The scoring runs entirely in your browser, using the words you paste, and nothing is sent to a server for processing.
Reference: Google Search Central