How to Automate Document Summarisation With AI
AI document summarisation turns a longer source into a shorter, structured result through a repeatable workflow.
A basic automation may look like:
Document
→ AI Summary
→ Reviewable Output
A more reliable process may also check the file, divide long content into sections, extract important details, combine partial summaries, validate the final result, and send uncertain cases to a person.
The goal is not simply to make a document shorter.
A useful summary should preserve the information that matters for the intended reader while making omissions, uncertainty, and missing details visible.
This guide explains how to design that process, choose an appropriate summary format, handle long documents, test the result, and build the workflow in Feluda.
Decide what the summary is for
Begin with the reader and the decision the summary should support.
Different users need different summaries from the same document.
A project manager may need:
- progress;
- blockers;
- decisions;
- owners; and
- deadlines.
A researcher may need:
- the main question;
- methods;
- findings;
- limitations; and
- source references.
A customer-support team may need:
- the main issue;
- stated account details;
- previous actions;
- urgency; and
- missing information.
A request such as Summarise this document leaves the model to decide what
matters.
A workflow should define the purpose before choosing the model or output length.
Choose a summary type
Document summaries can take different forms.
| Summary type | Best suited for |
|---|---|
| Overview | A short explanation of the complete document |
| Executive summary | Decisions, implications, risks, and next actions |
| Section summary | A separate summary for each part of a long document |
| Key-point summary | Important facts in bullets |
| Structured summary | Fixed fields such as Findings, Risks, and Actions |
| Query-focused summary | Information related to one question or topic |
| Comparative summary | Similarities and differences across documents |
Choose one format that matches the task.
A short overview is useful for orientation, but it may not preserve every date, number, condition, or exception.
A structured summary is easier to review and reuse in another workflow step.
Extractive and abstractive summarisation
Two common approaches are extractive and abstractive summarisation.
Extractive summarisation selects important sentences or passages from the source.
It can preserve original wording, but the result may feel disconnected when selected passages come from different sections.
Abstractive summarisation creates new wording that condenses the meaning of the source.
It can produce a clearer, more natural explanation, but the model may omit, simplify, or misstate information.
Many practical workflows combine both ideas.
The process may first extract important facts and quotations, then ask the model to prepare a readable summary from those verified details.
This gives the final summary a clearer foundation.
Define the expected input
Decide which documents the workflow should accept.
Consider:
- supported file types;
- whether the document contains selectable text;
- whether it is a scan or image;
- expected page count;
- supported languages;
- whether tables or images matter;
- whether several documents may be provided; and
- whether the content may be sensitive.
A workflow designed for short text reports may not handle a scanned hundred-page document correctly.
Do not assume that text extraction succeeded merely because a file opened.
Check whether headings, page order, tables, columns, footnotes, and special characters remain understandable after the document is read.
When the source is an image or poor-quality scan, text-recognition errors can change names, amounts, and dates before the summarisation step begins.
Prepare the document
Raw document content often needs preparation.
A preparation step may:
- remove repeated headers and footers;
- preserve headings;
- identify page or section boundaries;
- remove empty pages;
- convert tables into readable text;
- separate appendices;
- remove duplicate content;
- exclude irrelevant metadata; or
- reject an unsupported file.
Keep information that affects meaning.
Removing a repeated page header may be safe. Removing a section title, warning, footnote, or table label may make the source harder to interpret.
Preserve a link between the prepared text and its original page or section when important claims need to be verified.
Write a focused summarisation instruction
The instruction should tell the model:
- what to summarise;
- who the reader is;
- what to include;
- which format to use;
- how long the result should be;
- how to handle missing information; and
- what not to infer.
For example:
Summarise the project report for a manager.
Return:
1. an overview of no more than 120 words;
2. completed work;
3. current blockers;
4. confirmed decisions;
5. action items with Owner and Deadline; and
6. missing information.
Use only the source document.
Do not turn proposals into decisions.
If an owner or deadline is absent, write "Not provided."
This is easier to test than a general request for a concise summary.
Use structured output
A fixed structure makes the result easier to inspect.
For example:
Overview:
Main findings:
Decisions:
Risks:
Actions:
Missing information:
Source sections:
Structured output is also easier for another workflow step to use.
A report-generation step can consume the fields. A condition can check
whether Missing information is empty. A reviewer can compare each claim
with the referenced section.
Do not ask for more fields than the task requires.
An overloaded format can make the summary long and encourage repetition.
Handle short documents directly
A short document may fit into one model request.
The workflow can:
- receive the document text;
- check that content is present;
- send it to the selected model;
- return a structured summary; and
- ask a person to review important details.
Even simple workflows should handle:
- empty files;
- unsupported content;
- documents unrelated to the expected task;
- missing required sections; and
- invalid output formats.
Test with several document styles rather than one example.
Handle long documents in stages
Long documents may exceed the amount of text a model can process effectively in one request.
Even when the complete file technically fits, important information may be lost when too much content competes for attention.
A staged workflow can:
- divide the document by section or length;
- summarise each part using the same fields;
- preserve section or page references;
- combine the partial summaries;
- remove duplication;
- identify contradictions or gaps; and
- produce the final summary.
This is sometimes called a map-and-reduce or hierarchical summarisation approach.
Divide by meaningful sections when possible. A split that breaks a table, sentence, or argument can reduce accuracy.
The final combining step should not introduce facts that were absent from the partial summaries or source.
Preserve important details
Summaries often fail by simplifying information that should remain precise.
Pay special attention to:
- names;
- dates;
- amounts;
- percentages;
- conditions;
- exceptions;
- obligations;
- deadlines;
- source attribution; and
- statements of uncertainty.
Ask the workflow to extract these details separately when they matter.
For example:
Key facts:
Dates:
Amounts:
Conditions:
Exceptions:
Compare them with the original document before the summary is approved.
A clear paragraph should not replace a precise value.
Separate facts, interpretations, and suggestions
A summary may contain different types of content.
Keep them distinct.
Use sections such as:
- Facts stated in the document;
- Author's conclusions;
- Unresolved questions;
- Suggested next steps.
The model's proposed action should not appear as though it came from the source.
This is especially important for reports, policies, research, contracts, and customer records.
When the workflow makes an inference, label it as an inference and retain the supporting source.
Summarise multiple documents carefully
A multi-document workflow should not simply merge all text and request one summary.
First identify each source.
Then:
- summarise each document separately;
- preserve its title and source details;
- extract common fields;
- compare agreements and differences;
- identify contradictions;
- show missing evidence; and
- prepare a combined summary.
The final output should make it clear which source supports each important claim.
Documents may describe different periods, use different definitions, or reach conflicting conclusions.
A combined summary should preserve those differences instead of forcing one answer.
Add validation
Validation checks whether the output is usable.
A workflow may confirm that:
- the summary is not empty;
- every required heading is present;
- the length is within the requested range;
- dates use a consistent format;
- required fields have values;
- missing values use
Not provided; - every source is represented; and
- section references exist.
Fixed checks should use normal workflow logic rather than another model where possible.
Validation cannot prove that every sentence is accurate.
Important factual claims still need comparison with the source.
Review the summary against the source
Human review should focus on more than grammar.
Check whether the summary:
- reflects the document's main purpose;
- includes the information requested;
- preserves names, dates, and amounts;
- distinguishes decisions from proposals;
- retains important conditions and exceptions;
- represents uncertainty accurately;
- avoids unsupported claims;
- follows the requested format; and
- identifies missing information.
Review requirements should become stricter when the source affects legal, medical, financial, employment, security, safety, or other high-impact decisions.
AI can help a reviewer locate and organise information. It should not be treated as the final authority for specialist interpretation.
Protect sensitive documents
Review the complete data path before processing confidential material.
Check:
- whether the model is local or cloud-based;
- which provider receives the content;
- whether tools or external services are used;
- where temporary and final files are stored;
- what information is logged;
- who can access the output; and
- how long the source and summary are retained.
Remove information the task does not require.
A local model can keep model processing on your computer, but the workflow is only fully local when every source, tool, and destination remains local.
Local processing does not replace normal file permissions, device security, backups, and access controls.
Build the workflow in Feluda
Feluda can be used to test and build a repeatable document-summary process.
Begin in Workbench.
Use one representative document or a section of it. Test the instruction, output structure, and selected model.
Compare the result with the source.
Once the task is clear, open Studio.
A basic workflow can use:
Input → LLM → Output
The Input block receives the document text or supported source material.
The LLM block uses the selected provider, model, and summarisation instruction.
The Output block returns the result for review.
For a more detailed workflow, use focused steps such as:
Document Input
→ LLM Extract Key Facts
→ LLM Create Summary
→ Expression Check Required Fields
→ Output
LLM Extract is useful when names, dates, amounts, actions, or other fields should be identified before the narrative summary.
Expression can apply fixed checks without asking the model to make an exact decision.
Feluda can connect to supported cloud providers and compatible local model applications.
Select a model based on document length, instruction following, privacy, speed, supported attachments, and available hardware.
Test the Feluda workflow
Save the workflow and test it with varied examples.
Use:
- a normal document;
- a very short document;
- a long document;
- a file with missing sections;
- a document with tables;
- a document containing conflicting statements;
- an unrelated file; and
- an empty or unreadable input.
Review where the first unexpected result appears.
It may be caused by text extraction, document preparation, the AI instruction, the selected model, an output field, or a later validation step.
Use RunFlows to run the saved workflow with new material and review the result.
Consider scheduling only when the input source is dependable, failures are visible, and someone remains responsible for reviewing the summaries.
Common document summarisation mistakes
Avoid these mistakes:
- asking for a summary without defining its purpose;
- processing an unreadable scan without checking extracted text;
- sending an entire long document in one step without testing;
- removing headings and source references;
- allowing the model to invent missing details;
- treating an executive summary as a complete substitute for the source;
- merging several documents without preserving attribution;
- failing to validate dates, numbers, and names;
- sending sensitive files to an unsuitable provider; and
- scheduling the process before manual tests are dependable.
A summary is a navigation and decision-support aid.
It does not remove the need to read the source when complete detail or specialist interpretation matters.
Start with one document type
Choose one repeated document task.
Define the reader, fields, length, and review standard.
Test the instruction with several real examples that contain no sensitive information.
Build the smallest workflow that returns a useful result.
Add extraction, long-document stages, tools, or scheduling only when the simple version cannot meet the requirement.
A dependable summarisation workflow is specific about what matters, honest about what is missing, and easy to compare with the original document.