Extractive Text Summarizer
NewSummarize text using a transparent frequency-based sentence-scoring algorithm — no AI, just statistics you can inspect.
Summary
This is a real, transparent statistical algorithm (frequency-based extractive summarization, in the tradition of Luhn's 1958 method) — it doesn't generate new sentences or use AI. It scores each existing sentence by how many high-frequency, non-common words it contains, then returns the highest-scoring sentences in their original order. It works best on longer, well-structured text where a few sentences carry most of the key information.
Frequently Asked Questions
Is this using AI or a language model?
No — this is a classic frequency-based extractive summarization algorithm (in the tradition of Hans Peter Luhn's 1958 method), not an AI/LLM. It's fully deterministic and runs entirely in your browser: it never generates new sentences, it only selects and reorders a subset of your original sentences.
How does it decide which sentences to keep?
It counts how often each non-common word (excluding stopwords like "the", "and", "of") appears across the whole text, normalizes those counts, then scores each sentence by summing the normalized frequency scores of its words. Sentences containing more high-frequency, topically important words score higher and are more likely to be selected.
Why might the summary include a sentence that doesn't seem like the "most important" one to me?
Because the algorithm measures word frequency, not semantic importance or reasoning — it has no understanding of what the text actually means. It works best for well-structured text where the most frequently repeated key terms genuinely align with the main point, and can occasionally surface a sentence that's statistically frequent-word-heavy without being what a human reader would call the "most important."
Does the selected summary preserve the original sentence order?
Yes — after scoring and picking the top N sentences, they're re-sorted back into their original order in the source text, so the summary reads as a coherent (if condensed) excerpt rather than a shuffled list of disconnected high-scoring sentences.
What kind of text does this work best on?
Longer, well-structured prose (articles, reports, essays) with clear topical repetition tends to summarize well. Very short text, lists, or text without much word repetition will produce a less meaningful summary, since the algorithm relies on frequency signal that needs enough text to be statistically meaningful.