Summarise a PDF

Why this works

Our AI summariser reads the entire PDF and returns a structured summary: TL;DR, key points, decisions, and risks/open questions. Long documents are summarised in sections, then rolled up.

Long documents accumulate faster than you can read them. Most knowledge workers face a stack of unread PDFs at any given moment: the quarterly report you need to skim before a meeting; the contract draft a counterparty sent over the weekend; the 30-page research paper a colleague flagged as relevant; the policy update from compliance. Reading every one in full is impossible; skimming risks missing the one consequential paragraph that changes everything.

AI summarisation reframes the problem. Rather than choosing between full read and skim, you get a structured summary at the depth you need: a 3-line TL;DR for meeting prep, a single-page summary for daily triage, or a section-by-section detailed overview for documents you\'ll cite in your own writing. The summary surfaces the document\'s key claims, decisions, and open questions so you can decide — with informed context, not just topic familiarity — whether to commit to a full read.

What the summariser actually does, by depth. TL;DR (3 lines): the document\'s single most important takeaway, its central recommendation or finding, and any critical caveat or risk. Suitable for triage where you need to know whether a document is relevant at all. Standard (1 page): structured summary covering the document\'s purpose, main arguments or findings, key supporting evidence, and any decisions or recommendations. Suitable for understanding a document\'s content without reading it in full — the right call for routine consumption of reports, contracts, and articles. Detailed (section-by-section): walks through the document\'s structure, capturing each section\'s claims, evidence, and conclusions. Roughly 600–1000 words for a 40-page source. Suitable when you\'ll cite the summary in your own writing or make a decision that depends on the document\'s argumentation.

What the model is good at. Extracting key claims and decisions: the model is trained to identify what a document is actually saying versus what\'s background or padding. Numbers, dates, names, and stated commitments come through reliably. Summarising structured documents: reports, contracts, policy documents, and research papers — anything with a clear hierarchy of claims — summarise cleanly. Multi-language summaries: ask for the summary in any of 30+ languages the model supports; useful for documents in one language consumed by readers in another.

Where to verify. Critical numbers: any specific figure that matters for a decision should be verified against the source document, not relied on from the summary alone. Quotes attributed to specific people: AI summaries can occasionally compress multiple statements into one, blurring attribution. Negative claims: "the document does not address X" is harder to verify than positive claims; for completeness verification on a specific topic, search the document directly. Nuanced legal or technical conclusions: summaries are useful triage but not authoritative interpretation; for legally consequential or technically detailed content, treat the summary as a navigation aid, not a substitute for reading.

Document types where summarisation is particularly valuable. Long-form contracts and agreements: get the gist of clause-level structure before line-editing. Quarterly and annual reports: surface management commentary, key financial signals, and risk factors without page-by-page reading. Academic papers: triage relevance before committing to a full read — especially useful for literature reviews. Policy documents and compliance updates: catch what changed, what\'s required of you, and any deadlines, without absorbing the whole regulatory text.

A workflow note for stacks of similar documents. When you have 10+ PDFs of the same type (10 supplier contracts, 10 research papers, 10 monthly reports), the comparative value of summarisation rises sharply — you can read 10 one-page summaries in 20 minutes and then commit to fully reading only the 2 that warrant it. For document triage at scale, the Standard or TL;DR depths are usually the right pick rather than Detailed.

How it works