PDF Summarizer

Step 1 of 3

Opening the private summarizer session...

Choose a text-based PDF

Use one PDF up to 20 MiB and 150 pages. The tool reads its existing text layer; scanned pages need OCR first.

or drop one PDF here

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Large documents can take a moment. You can stop safely without keeping a partial result.

pages

Need a quick first pass through a report, paper or brief? This PDF summarizer reads the text layer already embedded in the file and ranks its sentences by recurring words. It then shows the selected sentences in their extracted order and lists up to 12 frequent terms. It does not use AI, rewrite the document, check facts or replace a careful reading. Your PDF and extracted text stay in the browser.

How to summarize a PDF

  1. 1

    Choose a text-based PDF

    Select one genuine PDF up to 20 MiB and 150 pages. A scan made only of page images needs OCR before it has text to summarize.

  2. 2

    Let the browser extract the text

    The tool reads the embedded text layer locally and stops if the document exceeds 2,000,000 extracted characters. Password-protected, damaged and unreadable files are rejected.

  3. 3

    Set the length

    Choose from 3 to 20 requested sentences. A short document returns every available sentence rather than inventing extra text.

  4. 4

    Review and copy

    Check the selected sentences and frequent terms against the original PDF, then copy the summary for your notes.

What an extractive PDF summary can tell you

An extractive summary keeps sentences from the source instead of composing new prose. This tool counts useful words, gives a small boost to sentences near the beginning, chooses the requested number of high-scoring sentences, then restores their extracted order.

A simple example

Suppose a four-sentence project brief says:

  1. The pilot reduced average support waiting time.
  2. The team also redesigned its training handbook.
  3. Waiting time fell again after the second rollout.
  4. The report recommends monitoring waiting time each month.

Because waiting time recurs, sentences 1, 3 and 4 may rank above sentence 2. With a three-sentence setting, the result keeps those sentences in the order 1, 3, 4. It does not write a new conclusion or decide whether the report is correct.

What the browser does

Stage Actual behavior
PDF reading Loads the embedded text layer with PDF.js; it does not run OCR
Sentence boundaries Uses the page language’s Intl.Segmenter rules when available, with a Unicode-aware punctuation fallback
Word boundaries Uses language-aware word segmentation where supported and filters a small locale-specific stop-word list
Ranking Scores sentences from recurring words and a modest opening-position boost
Output Keeps selected sentences in extracted order and lists up to 12 frequent terms

Important limits

  • The PDF text layer controls the result. Columns, tables, headers, footers and unusual font encoding can produce text in a different order from the page you see.
  • The page locale guides segmentation. The tool does not inspect the whole document to identify its language. A PDF written in another language may split or rank less cleanly.
  • Selection can remove context. A sentence copied exactly from the extracted text can still be misleading without the paragraph, table or qualification around it.
  • This is not fact-checking. The tool ranks repetition; it does not judge evidence, resolve contradictions or verify claims.
  • Large files are bounded. The limits are 20 MiB, 150 pages and 2,000,000 extracted characters.

Privacy in the three-step flow

The source PDF and extracted text remain in browser storage. The multi-step flow identifies that local record with a random-looking ID in the URL and expires it after 30 minutes. The page and PDF library still make normal asset requests, but those requests do not carry your PDF. The file is not sent to our servers or to a conversion service.

Frequently Asked Questions

No. It uses deterministic word-frequency scoring to select sentences from the extracted PDF text. It does not call a language model, paraphrase the source, create new claims or fact-check the document.

Not by itself. A scan that contains only page images has no embedded text layer for the summarizer to read. Run OCR first, then use the resulting searchable PDF.

PDFs store positioned text rather than ordinary paragraphs. Multi-column layouts, tables, repeated headers, footers and unusual fonts can make extracted text arrive in a different order. The tool preserves the extracted order, which is not always the visual reading order.

The tool asks the browser to segment sentences and words using the current page locale, including languages that do not separate every word with spaces. A Unicode punctuation fallback is available, but the tool does not automatically detect the PDF language, so matching the site language to the document gives the best boundaries.

Use one genuine PDF no larger than 20 MiB, with no more than 150 pages and no more than 2,000,000 extracted characters. Password-protected, damaged and unreadable PDFs are rejected.

No. The PDF and extracted text stay in your browser. In the three-step flow, a private browser record lasts up to 30 minutes and the URL contains only its opaque ID. Normal page and library assets are downloaded without carrying your file.

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