Sentence Extractor
Extract and number each sentence from text
What is Sentence Extractor?
Sentence Extractor is a free online tool that parses text and extracts individual sentences, numbering each one for easy reference. It intelligently identifies sentence boundaries using periods, exclamation marks, question marks, and other punctuation, while correctly handling abbreviations and decimal numbers. This tool is invaluable for language analysis, content editing, translation work, and academic research. Writers can review each sentence individually for clarity and length, translators can work through text sentence by sentence, and researchers can extract specific sentences for citation. The numbered output makes it easy to reference specific sentences in feedback or discussion. All processing runs entirely in your browser, so your text remains completely private.
How to Use
- Enter the text you want to extract sentences from.
- Sentences are automatically separated and displayed with numbers.
- Copy individual sentences or all sentences at once.
Tips & Best Practices
- Use numbered sentences to provide specific feedback on individual sentences during editing or review.
- Extract sentences from a long article to review each one for clarity and grammar.
- Copy extracted sentences into a spreadsheet for detailed analysis or translation tracking.
- Use this tool to count the exact number of sentences in a text for summary writing.
- Review sentence lengths to identify overly long sentences that need to be broken up.
Use Cases
Translation Work
Break source text into individual sentences for systematic translation.
Content Editing
Review each sentence individually to check for clarity, grammar, and consistency.
Academic Research
Extract specific sentences from research papers for citation or analysis.
Language Learning
Practice reading comprehension by analyzing text one sentence at a time.
FAQ
How are sentences separated?
Sentences are split by newlines, or at punctuation marks (.!?。！？) followed by whitespace. Sentences are also split when an uppercase letter or Korean character immediately follows punctuation without a space.
Can I copy individual sentences?
Yes, hover over any sentence to see the copy button on the right. You can also use 'Copy All' to copy all numbered sentences at once.
What is sentence tokenization?
Sentence tokenization is a fundamental step in NLP that divides continuous text into individual sentences by detecting boundaries at punctuation marks and line breaks.
Is my data collected?
No, all processing happens in your browser and no text is sent to any server.
Are periods after abbreviations (e.g., Dr., Mr.) treated as sentence boundaries?
Periods after abbreviations may be detected as sentence boundaries if followed by a space and uppercase letter. Please review the results for accuracy.
Does it handle mixed Korean and English text?
Yes, both Korean punctuation marks and English punctuation marks are recognized, so mixed Korean-English text is correctly separated.
How does sentence extraction work?
The tool identifies sentence boundaries using punctuation marks while handling special cases like abbreviations and decimal numbers.
Does it handle abbreviations correctly?
The tool uses heuristics to distinguish between abbreviation periods and sentence-ending periods, though some edge cases may need manual review.
Can it extract sentences from all languages?
It works best with languages that use standard punctuation for sentence endings.
Are numbered sentences useful for collaboration?
Yes, numbered sentences make it easy to reference specific parts of a text in comments or discussion.
Does it preserve the original text formatting?
The tool strips formatting and presents each sentence on its own line with a number prefix.
Can I extract sentences from a PDF?
Copy the text from your PDF and paste it into the input area. Note that PDF line breaks may affect sentence detection.
How are very short sentences handled?
All text ending with sentence-terminating punctuation is treated as a sentence, including short ones like 'Yes.' or 'OK!'