GPT-3: How to Summarize a PDF (70 000+ Words) 📔

A practical workaround for GPT-3’s 4,000-token limit turns a 73,000-word PDF into multiple usable outputs—key takeaways, a 15-step guide, a blog post draft, and even Midjourney-style illustration prompts. Using a Python pipeline, the workflow converts the PDF to text, slices the full text into 92 chunks, summarizes each chunk, merges the chunk summaries, and then re-summarizes the merged result to produce a coherent “compressed” version of the original book.

The example centers on Carl Newport’s Deep Work (190 pages, ~73,000 words). After running the script, the author reports that the process completed in about nine minutes on a PC—though the first attempt crashed mid-run and required a restart. Once finished, the outputs were evaluated in layers: first a set of “key notes,” then a step-by-step guide, then a blog post structure, and finally prompts intended for Midjourney illustrations.

The key notes were described as substantial and “quite a lot,” with early inspection suggesting the summaries captured the book’s substance well. The step-by-step guide distilled the book into 15 actionable steps for performing deep work, including concrete prescriptions such as setting a hard deadline for deep tasks, working with high intensity, and creating a ritual with rules and processes to keep effort structured. Other steps highlighted include implementing the Craftsman approach and using tool selection as a way to align methods with personal and professional goals.

A blog post draft was also generated from the notes, with a clear outline: an introduction; headline strategies to maximize deep work; discussion of open office design impacts; the Craftsman approach; tool selection; the “law of the vital few”; and a shutdown ritual to close out the workday. The blog post needed “some work,” but the structure and coverage were considered solid enough to serve as a starting point.

Illustration prompts for Midjourney were the weakest output in the set. The prompts produced themes like “deep work productivity and focus improvement” and “work deeply break from focus drain the shallows,” but the results were judged as not particularly successful. Still, the pipeline demonstrated that the same condensed notes can be repurposed across formats—even if creative prompt generation may require additional tuning.

Overall, the core finding is that long-form PDFs can be transformed into multiple downstream artifacts by chunking, hierarchical summarization, and iterative re-summarization—turning a token-limited model into a practical document-to-content system.