Structure-aware summarization

A PDF content summarizer that keeps the outline — section by section, not flattened into a blob.

Most summarizers concatenate everything and hand back one paragraph that loses the document's shape. This one detects Abstract, Methods, Results, clauses, and chapters individually — then writes a TL;DR per section so the original hierarchy survives.

account_treeHierarchical output format_list_bulletedPer-section TL;DR linkSection-scoped citations descriptionDOCX / MD / PDF export
auto_awesomeOpen the summarizer arrow_downwardHow section detection works

Structure preserved, not flattened.

A 40-page PDF isn't 40 pages of one thing — it's an outline. The summarizer should return an outline too.

Most LLM summarizers chunk a PDF, summarize each chunk, and concatenate the result into one prose paragraph. That output is convenient for tweets but useless for documents that have shape — research papers, contracts, board reports, multi-chapter handbooks.

A structure-aware summarizer instead detects the document's actual hierarchy first — Abstract, Methods, Results, Discussion, or Clause 1, Clause 2, Clause 3 — and writes one TL;DR per detected section. The output is itself an outline, mirroring the source.

The difference matters when you need to find something. With a flat blob you re-read the whole summary to locate the part about pricing. With per-section TL;DRs you jump straight to "Clause 4 · Pricing" and find a 2-line answer with a link back to the source paragraph.

blockFlat blob output
account_treeSection-aware
articleAbstract
scienceMethods
analyticsResults
forumDiscussion

Built for documents with shape.

If your PDF has chapters, clauses, line items, or agenda blocks, a per-section summary preserves what a flat one destroys.

science
Research papers
IMRAD structure preserved — Abstract, Introduction, Methods, Results, Discussion each get their own TL;DR with section-scoped citations.
IMRAD
gavel
Contracts
Each clause is summarized independently — Term, Pricing, Liability, Termination — so you can scan obligations clause-by-clause.
Per-clause
balance
Legal briefs
Statement of Facts, Argument I, Argument II, Conclusion — preserved as discrete blocks instead of merged into a single narrative.
Sectioned
trending_up
Financial reports
Revenue, Operating Expenses, Cash Flow, Risk Factors — each line item summarized with the underlying numbers attached.
Line items
groups
Meeting transcripts
Agenda items become sections — each gets a decision-and-action TL;DR, so attendees see what was concluded per topic.
Per-agenda

How section detection works.

Heading detection is a typography problem before it's a language problem. The pipeline reads the page like a designer would, then summarizes like an editor would.

1
PDF parsing
Extract the text layer with positional metadata — every span gets x, y, fontSize, weight, and page. Scanned PDFs are OCR'd first so the same metadata exists.
2
Heading detection
Cluster spans by typography: bigger font + bolder weight + leading whitespace = heading candidate. Numbering patterns (1.1.2, I.A) confirm hierarchy depth.
3
Semantic block grouping
Body paragraphs are assigned to the nearest preceding heading. For PDFs without explicit headings, embeddings detect topic shifts and synthesize block labels.
4
Per-section abstractive summary
Each block is summarized independently with section-scoped context — no cross-bleed. Citations are attached at paragraph granularity within the block.

Output formats — pick the shape you need.

Same hierarchical extraction, three rendering modes. Switch between them without re-summarizing.

format_list_bulleted
Bullet TL;DR
Three to five bullets per section. Optimal for scanning, briefing decks, and follow-up email digests where readers need to skim by topic.
Methods
Two-stage retrieval pipeline
N=412 clinical PDFs sampled
ROUGE-L primary metric
subject
Executive paragraph
One tight paragraph per section, written for prose readers. Preserves connective logic between findings — useful for memos and reports.
Results
The section-aware variant outperformed flat baselines by 18 ROUGE-L points and held a 96% section-attribution accuracy on held-out documents.
account_tree
Outline / mind-map
A collapsible tree of sections and sub-sections — best for long PDFs where you want to navigate first and read second.
Paper
Abstract
Methods
Sampling
Pipeline
Results

What you get vs a flat summary.

Both produce text. Only one preserves the document.

Flat blobTypical summarizer
One paragraph for the whole document
  • closeLoses the outline. Methods and Discussion get blurred into the same prose stream.
  • closeCross-section citations. A claim from Results may be attributed to a passage in Methods.
  • closeNo navigation. You re-read the summary to find a topic.
  • closeLength collapses meaning. A 40-page contract becomes 200 words; clauses disappear.
  • closeHard to export structurally. The Word doc has no headings.
Section-awareThis tool
One TL;DR per detected section, hierarchy intact
  • checkOutline preserved. Each Abstract, Method, clause, or chapter has its own block.
  • checkSection-scoped citations. A bullet in Methods cites only Methods passages.
  • checkJump to topic. Click "Clause 4" and read 60 words instead of re-scanning the whole summary.
  • checkLength adapts to depth. Long sections get longer summaries automatically.
  • checkStructural export. DOCX with H1/H2 styles, Markdown with proper heading levels.

When section-aware actually matters.

A two-page memo doesn't need this. A forty-page contract does.

menu_book
Long technical PDFs
When the document is 40+ pages with distinct phases (background, design, evaluation), a flat summary collapses the phases into one undifferentiated paragraph and you lose the ability to skim by topic.
group
Multi-author papers
Each contributor wrote a different section in a different voice and with different terminology. Per-section summaries respect those boundaries instead of forcing a fake unified narrative.
gavel
Contracts where each clause counts
In a 30-clause MSA, every clause is a separate negotiating surface. Lumping Pricing and Termination into the same blob hides the things you actually need to redline.

Pair it with the rest of the privacy stack.

Summarization is one piece — the other tools handle the document around it.

Chat with PDF
Ask follow-up questions section by section after reading the TL;DR.
Translate PDF (AI)
Translate the per-section summary into 30+ languages with structure intact.
Compress PDF (no upload)
Shrink the source PDF locally before sharing alongside the summary.
Secure PDF transfer
Send the original document and the summary together via end-to-end encrypted link.
PDF to Word (local)
Convert the source PDF to editable Word so you can edit alongside the summary.

Frequently asked questions

How does the summarizer detect sections in a PDF?
Section detection combines typography analysis (font size jumps, weight changes, all-caps usage) with positional cues (vertical spacing, indentation, numbering patterns like 1., 1.1, I., A.). The parser extracts a heading tree from the PDF's text layer, validates it against page geometry, and groups paragraphs into the section they belong to. The result is a hierarchical outline that drives per-section summarization. See the technical flow for the four-stage pipeline.
Can I get one summary per chapter instead of one for the whole document?
Yes — that's the default behavior. The summarizer treats each detected section (chapter, clause, IMRAD block, agenda item) as its own unit and produces an independent TL;DR for it. You also get a roll-up executive paragraph at the top, but the per-section breakdown is the primary output and can be exported on its own. Open the tool at /summarize-pdf-ai to try it.
What if my PDF doesn't have explicit headings?
For documents without typographic headings (plain prose, scanned articles, transcripts), the tool falls back to semantic block grouping: paragraphs are clustered by topic shift detected in embeddings, then assigned synthetic section labels. The output is still hierarchical — you get topic-grouped TL;DRs instead of arbitrary chunk-by-chunk summaries.
Can I export the section summaries as a Word doc?
Yes. Export options include Word (.docx) with proper heading styles applied, Markdown with H1/H2 hierarchy intact, plain text, and PDF. The Word export keeps the section structure so you can drop it into a report or briefing template without re-formatting. If you also need the original PDF in editable form, use PDF to Word (local) alongside the summary.
Does each section summary include its own source citations?
Yes. Each per-section TL;DR carries page-and-paragraph anchors back to the source PDF, so a bullet in the Methods summary cites the exact passage in Methods (not somewhere in Results). Click any bullet to jump to its highlighted source span in the inline viewer. Citations are scoped to the section, which prevents cross-section attribution errors that flat summarizers commonly make. To dig deeper into any section, switch to chat mode and ask follow-ups.

Stop reading forty pages. Start reading forty TL;DRs — one per section.

Drop a PDF, watch the outline appear, get a per-section TL;DR with section-scoped citations. Export to Word, Markdown, or back to PDF — structure intact.

auto_awesomeOpen the summarizer