To summarize notes with AI without losing context, label what the notes cover, preserve definitions and conditions, generate a shorter draft, and compare it line by line with both the notes and the original source. Keep source locations, exceptions, uncertainties, and open questions in the compressed version. A shorter output is useful only when it remains traceable; polished wording cannot repair incomplete input.
Creator disclosure: Heni Hazbay creates Summarise Visually and has a direct interest in the app. This guide gives a general context-preservation method before describing the app’s bounded role.
Publication and evidence note: This guide was prepared from the cited sources and reviewed project evidence. Its checklist and fictional before-and-after example are editorial tools, not an accuracy result or a captured product test.
First decide whether the notes are ready to compress
This workflow begins with notes you already made. If you are creating notes directly from a lecture, chapter, or paper, use the separate guide on how to use AI to make study notes. Compression has a different risk: it can make an incomplete, uncertain, or poorly sourced notebook look finished.
Before using any tool, write a coverage statement:
These notes cover:
They do not cover:
Original source and version:
Pages, sections, or timestamps checked:
Visuals or equations not represented in text:
Unresolved questions:
Purpose of the shorter version:If you cannot fill in the original source or coverage fields, do not label the result as a source-checked summary. You can still organize personal notes, but the final text should remain clearly marked as a summary of those notes rather than a summary of the full source.
Before supplying notes to any AI service, apply the relevant privacy, ownership, and institution rules. UNESCO’s guidance for generative AI in education and research uses a human-centred policy frame and discusses issues including data privacy and human agency. It does not approve a particular tool, override local rules, or validate this compression checklist.
The context-preservation checklist
Protect the details most likely to disappear during compression. Copy this checklist beside the generated draft and mark every item kept, corrected, not applicable, or needs source review.
Source and scope
- Is the title, speaker or author, date, edition, and selected range still visible?
- Does the result say that it summarizes notes rather than claiming to cover an entire source?
- Are gaps in attendance, reading, transcription, or note-taking preserved?
Meaning
- Are important terms defined rather than merely named?
- Are conditions, exceptions, comparisons, and sequence relationships retained?
- Are the source’s statements separated from your own interpretations?
- Are negation and degree preserved, such as “may” versus “will” or “associated” versus “caused”?
Evidence and location
- Does each consequential point retain a page, section, timestamp, figure, or other locator?
- Are quotations still exact and visibly marked?
- Are examples labelled as examples rather than general rules?
- Are figures, tables, equations, or demonstrations flagged for direct inspection?
Uncertainty and action
- Are questions, contradictions, and uncertain notes still visible?
- Does the shorter version identify what needs checking?
- Can you turn the checked points into answer-hidden retrieval prompts?
- Is the summary short for a defined purpose rather than short at any cost?
This checklist is the quality gate. If a one-line version cannot keep an essential exception or definition, choose a longer format.
A reliable compression sequence
1. Clean the input without rewriting its meaning
Remove accidental duplicates, navigation clutter, and formatting noise. Keep uncertainty markers such as “check this,” question marks, and source gaps. Standardize headings only when you understand what belongs under them. Do not silently fill a missing definition from memory.
Separate course instructions, source notes, your interpretations, and action items. An AI system cannot reliably preserve a distinction that is invisible in the input. Labels such as SOURCE, MY INTERPRETATION, QUESTION, and VISUAL CHECK make the boundary explicit.
2. Choose the summary’s job and length
A revision card, seminar briefing, handover note, and long-term reference need different detail. Define the output by use rather than an arbitrary word count. For example: “Create a one-page review map that retains every definition, condition, exception, and source locator. Keep unresolved questions in a separate section.”
Avoid prompts that request only “the most important points” without defining importance. A surprising detail, safety condition, or exception can be essential even when it appears once.
3. Generate one labelled candidate
Save the original notes and the unedited generated output. Label the result “candidate summary of notes, not yet source-checked.” Do not overwrite the only copy of the notes. If sensitive or restricted material is involved, follow the relevant privacy, course, workplace, and tool-use rules before supplying it.
4. Run an omission comparison
Compare headings first: every major input section should either appear in the output or be intentionally excluded with a reason. Then compare atomic points. Mark missing definitions, conditions, counterexamples, source locations, and questions. Finally, inspect whether the shorter wording changes who did what, how certain the statement is, or where it applies.
The AI summary accuracy guide gives a broader supported, unsupported, omitted, duplicated, and ambiguous labelling method. Use the original source to resolve important discrepancies; asking for another generated version is not verification.
5. Write the final version from checked material
Correct the candidate using the source. Add back necessary context even if it makes the result longer. Retain a short coverage line at the top and an open-questions section at the bottom. Archive the source notes beside the summary so future you can inspect what was compressed.
Before-and-after example with omissions annotated
The following fictional notes were written for this guide. They are not an app output and do not describe a real course or research finding.
Before: fictional source notes
Topic: Harbor signal procedure, fictional training example
Coverage: pages 4–7 only; diagram on page 6 not copied
Default sequence: observe indicator, confirm channel, send status message.
Exception: if the channel is marked unavailable, record the fault and do not send.
"Confirmed" means a second operator has repeated the channel identifier.
Example on page 5 uses Channel B; it is an example, not the required channel.
My interpretation: the confirmation step seems intended to prevent selection errors.
Question: does the night procedure use the same indicator?
Visual check: page 6 diagram may add a branch after a fault.Too-short AI-style candidate
Check the indicator, use Channel B, confirm it, and send a status message.The candidate is smooth but unsafe as a study record. “Use Channel B” converts an example into a rule. The unavailable-channel exception is omitted. The definition of “confirmed” is lost. The writer’s interpretation disappears rather than remaining labelled. The open question, coverage boundary, and missing diagram are hidden. “Confirm it” also makes the object of confirmation ambiguous.
Checked compressed version
Coverage: fictional pages 4–7; page 6 diagram still needs direct review.
Default sequence: observe the indicator, confirm the channel by having a
second operator repeat its identifier, then send the status message.
Exception: when the channel is marked unavailable, record the fault and do
not send. Channel B is only the page 5 example.
Open question: whether the fictional night procedure uses the same indicator.
Writer interpretation, not source claim: confirmation may be intended to
reduce selection errors.The checked version is longer than the candidate because the context is part of the meaning. Compression is successful here because duplication and scattered wording were reduced while the scope, definition, exception, example boundary, uncertainty, and visual check remained visible.
Turn the checked summary into a review tool
Do not stop at a neat paragraph. Move key terms into a cue column and write questions that can be answered without looking. Cornell University’s explanation of the Cornell Note Taking System describes separate cue, notes, and summary areas along with reciting, reflecting, and reviewing. This page adapts that separation to an AI-assisted compression check; Cornell University is not presented as evaluating AI summaries or this workflow.
For each checked point, write one prompt that preserves the boundary. “What is the sequence?” may miss the exception; “What is the default sequence, and when does it stop?” tests both. Keep the answer hidden during the attempt, then correct from the source. The guide on using Key Points and Q&A for study explains that feedback loop.
If you need a reusable claim-and-context structure for an article rather than class notes, use the article summary template. Different source types need different verification fields.
A bounded Summarise Visually workflow
Current project review supports supplied text and selected summary modes, including Detailed, Key Points, and Q&A. That evidence does not establish factual accuracy, complete context preservation, visual interpretation, automatic source locations, hidden-answer testing, or compatibility with every source.
Begin with notes you are permitted to supply and retain an untouched copy. Include the coverage statement and visible labels for source material, personal interpretation, questions, and visual checks. Use Detailed for a candidate prose condensation, Key Points for a candidate review map, or Q&A for candidate prompts. These are organizational choices, not accuracy guarantees.
Compare the result with the context-preservation checklist. Add source locations and corrections yourself. In Q&A, check both sides of every pair and create an answer-hidden attempt outside the visible result if needed. Do not present the generated output as covering a lecture, chapter, or paper when the input was only your partial notes.
Limits and verification
- An AI compression can make incomplete or inaccurate notes appear complete and authoritative.
- The output cannot restore source context, a diagram, a missed lecture segment, or a condition that never appeared in the input.
- Generated text can weaken qualifiers, merge concepts, reverse relationships, or convert examples into general rules.
- A source locator supports checking but does not prove that the note or interpretation is correct.
- Course and institution rules decide whether AI assistance is allowed and how it must be disclosed.
- Notes are not professional advice, and consequential topics require the original source and appropriately qualified guidance.
Verify the coverage statement, every definition and condition, all consequential claims, and every unresolved question against the original source. Retain the uncompressed notes and the unedited candidate as an audit trail. If a visual or equation affects meaning, inspect it directly rather than relying on a text summary.
Sources and evidence
The note-organization discussion draws on Cornell University’s Cornell Note Taking System, while UNESCO’s generative-AI guidance supplies the human-centred policy context. Both were accessed July 20, 2026. The context-preservation checklist and fictional before-and-after example are original editorial assets for this guide; they are not validated instruments or captured product results.
Product statements are limited to reviewed project evidence for supplied text and Detailed, Key Points, and Q&A modes. No accuracy rate, complete-source guarantee, OCR, automatic citation, learning outcome, or current-device note-compression walkthrough is claimed. The site’s Methodology explains the product-evidence standard.