Content Summarizer

Extractive summarization — pick the most informative sentences from your article using word-frequency scoring and position weighting.

Summary


      

    

    

      
Compression Stats

      

    

  


  

  

    
How Extractive Summarization Works

    
Extractive summarization picks sentences from the original text; it doesn't paraphrase. Three factors weight each sentence's score:

    
    
      
  • Word frequency — sentences containing the most distinctive words (non-stop, non-rare) score higher.
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  • Position — first and last sentences of paragraphs tend to carry the argument. Leading paragraphs get a bonus.
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  • Length — very short sentences are informative but thin; very long ones dilute. Sentences 10–30 words score best.
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What You Get

    
Sentences are scored, the top N are selected, then returned in their original order so the summary still reads as connected prose. No facts are invented because no new text is generated.

    
Limitations

    
Extractive summaries sometimes duplicate ideas (two sentences making the same point), or skip the middle where the actual argument was. For a short high-signal summary of a long article, this is usually fine. For a genuinely tight TL;DR, you may need to edit the output.

  















Summarizer FAQ




Extractive vs abstractive — what's the difference?
Extractive picks sentences from the original. Abstractive writes new sentences that capture the meaning. Abstractive is what ChatGPT does; extractive is what this tool does — and it never hallucinates.


Why doesn't it paraphrase?
Abstractive summarization requires a language model. This tool runs 100% client-side with no ML model download, so it uses deterministic statistical scoring instead.


What length should I choose?
25% is a good default. For a 2,000-word article that gives ~500 words, which fits on one screen. For longer content, drop to 15%.


Why does it skip the middle sometimes?
Position weighting gives a boost to leading sentences. If your article has its key facts in paragraph 8, extractive scoring may miss them. Always skim the output.