Detect repeated sentence groups locally, see occurrence counts and sentence positions, and copy a compact editing report without semantic or plagiarism claims.
This is exact normalized matching, not plagiarism detection or semantic near-duplicate detection. Text stays in this browser.
Start with the broad counter that matches the question, then move to focused frequency, reading-time or syllable analysis. Specialist readability, repetition and writing-goal tools remain available from the full hub.
Paste text, choose whether case and whitespace differences should matter, then review exact repeated sentence groups and their sentence positions.
Sentence boundaries use the browser’s sentence segmenter when available with a simple fallback. Results are exact normalized duplicates, not semantic similarity or plagiarism analysis.
Use the tool first, then apply these checks to verify the inputs, interpret the result, and hand it off without displacing the primary workflow.
Case folding, whitespace normalization and punctuation handling can change duplicate counts. Record the selected normalization options when comparing revisions so a lower count is not caused merely by different matching rules.
Repeated sentences may be accidental boilerplate, but they can also be intentional labels, legal language, quotations or recurring instructions. Use the finder to locate candidates, then decide whether repetition is harmful by reading the surrounding paragraphs.
An exact sentence matcher will miss paraphrases and tiny edits, while fuzzy matching can create false positives. If content originality or editorial consistency matters, combine exact duplicate detection with a separate similarity review rather than stretching one metric beyond its design.
When removing repeated text from a long document, preserve the original and compare sentence counts after editing. This makes it easier to catch an accidental deletion where a repeated sentence was necessary in more than one section.
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