# Free AI PDF Summarizer: See Sentence Extraction Live URL: https://aginsi.com/tools/ai-pdf-summarizer Type: tool Locale: en Published: 2026-09-25 Updated: 2026-09-26 --- > Paste text copied from a PDF and this free AI PDF summarizer scores every sentence by how much of the document's vocabulary it carries, then keeps only the ones that matter. ## See How an AI PDF Summarizer Picks the Sentences That Matter Paste text from any PDF and this free widget scores every sentence the way extractive AI summarizer tools do, live, in your browser. No upload required. ## AI PDF summarizer, live Paste text copied from your PDF, pick a length, and watch the widget score and keep the sentences that carry the most weight. *[Interactive widget — see the live page for the full experience]* ## What the AI PDF summarizer actually scores ### Frequency scoring Every word in the pasted text is counted, minus a short list of connecting words ("the", "and", "of"). Words that repeat often across the document score higher, and a sentence's score is the average of its words' scores. ### Whole sentences, not new ones This is extractive summarization: the output is made of sentences lifted verbatim from what you pasted, never rewritten. For citation-sensitive reading, that traceability back to the exact wording matters more than fluency. ### Adjustable length, with the math shown Pick 3, 5 or 8 sentences and the stat row updates: words in, words out, percent shorter, and estimated reading time saved against a 200 words-per-minute baseline. No hidden step between your text and the number. ## What a one-page widget cannot do 1. **It will not read tables, figures or footnotes** — The scoring only sees the prose you paste. Numeric tables, figure captions and footnoted asides are either skipped or scored as if they were ordinary sentences, which usually means they get dropped. 2. **It will not preserve document structure across sections** — A 12-page report with five sections gets treated as one pool of sentences. For a document that long, run each section through separately rather than the whole PDF at once, so the summary still reflects each part. 3. **It will not tell you if a claim is accurate** — Extraction ranks sentences by vocabulary weight, not by truth. A confidently wrong sentence and a carefully hedged one can score the same if they share vocabulary with the rest of the document. ## Questions about the extraction logic ### Is this the same technique full AI PDF summarizer products use? It is one half of it. Extractive scoring, ranking existing sentences by how much of the document's vocabulary they carry, is one of the two main families of summarization, alongside abstractive methods that generate new phrasing. Most commercial AI PDF summarizer tools combine both, plus structure detection (headings, tables, figure captions) and multi-document synthesis that a single browser widget does not attempt. ### Does this work on scanned PDFs or photographed pages? No. The widget needs a text layer to score, so paste the text after copying it out of your PDF reader. A scanned or photographed page has no underlying text until it goes through OCR first, which is outside what a client-side widget can do. ### Where does the reading-time estimate come from? It applies 200 words per minute, a commonly cited baseline for adult silent reading of general prose. Dense academic or technical writing reads slower for most people, so treat the estimate as a rough anchor, not a personal measurement. ### Is my pasted text stored or sent anywhere? No. The scoring runs entirely in your browser tab. Nothing you paste is uploaded to a server. The page logs an anonymous, one-time signal that the widget was used, not the text itself. ### Why does the summary sometimes keep two sentences that say almost the same thing? Because extractive scoring picks whole sentences rather than rewriting them. It does not merge overlapping ideas the way an abstractive model would. If the source repeats a point in different words, both instances can score highly on their own. ### Does it work on languages other than English? The frequency scoring itself is language-agnostic, since it counts repeated words regardless of language. The filler-word list this demo excludes ("the", "and", "of", and similar) is tuned for English, so text in other languages skips fewer of those connecting words, which can slightly dilute the ranking. ### What's the practical difference between short, medium and long? They map to 3, 5 and 8 extracted sentences. A short document already close to 3 sentences will look nearly unchanged; a long report gives the longer setting more material to be selective about. Match the setting to how aggressively you want to cut. ### How is this different from an abstract or a table of contents? An abstract is written by the author, in advance, about their own framing of the work. This widget derives its output strictly from the text you paste, after the fact, which is useful for a report, a grey-literature document, or a paper that never had an abstract to begin with. ## Want more AI reading tools like this one? Aginsi covers AI research summarization tools test by test, comparison by comparison, for people who read for a living. *Call to action: Browse Aginsi's tools and reviews* ## FAQ ### Is this the same technique full AI PDF summarizer products use? It is one half of it. Extractive scoring, ranking existing sentences by how much of the document's vocabulary they carry, is one of the two main families of summarization, alongside abstractive methods that generate new phrasing. Most commercial AI PDF summarizer tools combine both, plus structure detection (headings, tables, figure captions) and multi-document synthesis that a single browser widget does not attempt. ### Does this work on scanned PDFs or photographed pages? No. The widget needs a text layer to score, so paste the text after copying it out of your PDF reader. A scanned or photographed page has no underlying text until it goes through OCR first, which is outside what a client-side widget can do. ### Where does the reading-time estimate come from? It applies 200 words per minute, a commonly cited baseline for adult silent reading of general prose. Dense academic or technical writing reads slower for most people, so treat the estimate as a rough anchor, not a personal measurement. ### Is my pasted text stored or sent anywhere? No. The scoring runs entirely in your browser tab. Nothing you paste is uploaded to a server. The page logs an anonymous, one-time signal that the widget was used, not the text itself. ### Why does the summary sometimes keep two sentences that say almost the same thing? Because extractive scoring picks whole sentences rather than rewriting them. It does not merge overlapping ideas the way an abstractive model would. If the source repeats a point in different words, both instances can score highly on their own. ### Does it work on languages other than English? The frequency scoring itself is language-agnostic, since it counts repeated words regardless of language. The filler-word list this demo excludes ("the", "and", "of", and similar) is tuned for English, so text in other languages skips fewer of those connecting words, which can slightly dilute the ranking. ### What's the practical difference between short, medium and long? They map to 3, 5 and 8 extracted sentences. A short document already close to 3 sentences will look nearly unchanged; a long report gives the longer setting more material to be selective about. Match the setting to how aggressively you want to cut. ### How is this different from an abstract or a table of contents? An abstract is written by the author, in advance, about their own framing of the work. This widget derives its output strictly from the text you paste, after the fact, which is useful for a report, a grey-literature document, or a paper that never had an abstract to begin with.