Summarize 200-Page PDFs in 30 Seconds with AI
Use retrieval-augmented AI to turn long PDFs, contracts, and research papers into instant summaries with citations.
Reading a 200-page contract or research paper is a bad use of a Tuesday. Modern AI can extract the key points, decisions, and open questions in under a minute — if you feed it the document the right way.
Why 'paste-into-ChatGPT' fails at scale
Consumer chatbots have context windows measured in pages, not chapters. Pasting a 200-page PDF truncates silently and the model hallucinates confident-sounding nonsense about the missing parts. The fix is retrieval-augmented generation (RAG): index the whole document, retrieve only the chunks relevant to each question, then summarize.
The pipeline
- • 1. Extract text — parse the PDF (pdf-lib for born-digital, Tesseract for scans).
- • 2. Chunk — split into 500–1000 token blocks with 10% overlap so no idea is cut in half.
- • 3. Embed — turn each chunk into a vector with an embedding model.
- • 4. Retrieve — for each question, pull the top-k relevant chunks.
- • 5. Summarize — hand the chunks to an LLM with a strict 'cite the page number' prompt.
What to summarize
Legal contracts: obligations, termination clauses, indemnity, governing law. Research papers: hypothesis, method, results, limitations. Financial filings: revenue drivers, risks, forward-looking statements. Pick a template per document type — generic 'summarize this' outputs are always worse.
Try it
Be My PDF's AI tools run OCR then summarization in one pass, and every claim links back to the source page. Combine with OCR for scanned PDFs when your document is image-based.
Frequently asked questions
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