Pull the important sentences out of a wall of AI text
LLM responses are often three times longer than they need to be. When you only want the substance, you can either re-prompt for a summary or extract the key sentences yourself. This tool does the latter instantly and locally: it ranks every sentence in your text and surfaces the most information-dense ones, with a relevance score so you can see why each made the cut.
How it works
The text is split into sentences and tokenized into lowercased words. The tool builds a term-frequency map and weights each term by how distinctive it is, the core of TF-IDF — words that appear everywhere count for little, while specific, content-bearing words count for more. Each sentence’s score is the sum of its term weights normalized by length, plus a small position bonus because the first and last sentences of a passage tend to carry the thesis and conclusion. The highest-scoring sentences are then re-sorted into their original order so the summary still reads coherently.
Tips and notes
Extractive summaries keep the author’s exact wording, so they never hallucinate or distort claims — a genuine advantage over LLM rewrites when accuracy is critical. They do, however, read a little choppier because sentences are lifted out of context. For best results, paste clean prose rather than heavily bulleted or tabular output, since the scorer is tuned for sentences. Start with three to five key points for a paragraph-length input and scale up for longer documents. The output makes a great seed for a follow-up “expand on these points” prompt.