Meeting Summary: How to Break Down Meeting Text into Decisions, Questions, and Tasks
The Meeting Summary service solves a narrow task: it takes meeting text and splits it into decisions, open questions, and follow-up actions. It is not a replacement for a protocol and not a meeting participant, but a tool for primary processing. This article will explore how to distribute work between the service and a human, what fields to fill in, what a realistic result looks like, and where applicability boundaries lie.
Open tool: Meeting summary ↗What the Service Actually Does
Meeting Summary accepts text in the 'Source material' field and a goal in the 'Goal or desired result' field. The output is a single result in a separate panel: decisions, open questions, and tasks. The format is plain text, which can be copied or downloaded as TXT. There are no style presets, sliders, or levels of detail adjustment in the interface.
It is important to understand that the service does not record audio and does not participate in the meeting. You insert the transcript or notes yourself. If you have an audio recording, it must first be converted to text by another tool. The service works only with what you paste into it.
Volume limits also need consideration: the first field accepts up to 8,000 characters, and the goal field up to 1,000. If the meeting was long, split the text into parts and process them in blocks, then manually combine the results.
Source material: 'Anna will prepare the draft by Friday. Sam will check by Monday. The launch date has not been determined yet.' Goal: 'Extract decisions, questions, and tasks'.
Which Parts of the Task to Delegate to the Service
The service handles routine analysis well: finding explicit agreements in the text, extracting instructions with responsible persons and deadlines, and marking questions that remain unanswered. This saves time on initial sorting.
It is reasonable to delegate to it a rough classification when the text is already in readable form: filler words are removed, typos are corrected, discussions are separated from decisions. The cleaner the input, the more useful the output.
Do not expect the service to understand hints, irony, or informal agreements that were not spoken directly. If a decision sounded like 'well, I guess we'll do it that way,' the model might not recognize it. Such places are better marked in the text in advance.
Before pasting, mark in the text: 'DECISION:', 'QUESTION:', 'TASK:'. This simple technique increases parsing accuracy.
What to Check Manually
After generation, always verify the result against the source text. The model may assign a task to the wrong person, change a deadline, or turn a discussion into a decision. This is not a service error, but a normal limitation of language models.
Check three things: who is responsible, what the deadline is, and what exactly counts as completed. If the text had no deadline but one appeared in the result — this is a fabrication, it needs to be removed.
Separately check the open questions. Sometimes the model records things that were already decided on the meeting but were formulated unclearly. Verifying with the text takes a few minutes but saves you from an incorrect protocol.
If the result says 'Sam will check by Monday,' but the text said 'Sam will look at it next week,' clarify the wording manually.
When the Service Is Not Suitable
The service will not suit if the meeting was mainly emotional, without specific agreements. The output will be empty or nearly empty, and that is normal — there is nothing to process.
Do not use it for legally significant protocols, financial decisions, or medical consultations. In such cases, a human who takes responsibility for the formulations is needed. The service can help with a draft, but it does not replace specialist verification.
It also does not suit very long recordings: the 8,000-character limit means multi-hour meetings must be split into parts. There is no automatic batch processing; each part must be run separately.
If the text contains only general discussions without decisions and tasks, the result will contain empty sections. This is a signal that the material is not suitable for such analysis.
How the Working Process Looks
First, prepare the text: remove unnecessary replays, correct typos, if necessary place markers. Then paste it into the 'Source material' field and formulate the goal briefly, for example: 'Extract decisions, open questions, and tasks with responsible persons.'
Press Generate and wait for the result. It will appear in a separate panel. Copy the text or download it as TXT. If the result is incomplete, try splitting the original into meaning blocks and processing them in order.
After generation, conduct manual verification: mark what is confirmed by the text, what needs clarification, and what is unnecessary. Only after this can the result be considered a ready draft of a protocol.
Goal: 'Gather decisions, questions, and tasks with indication of responsible persons and deadlines.' Source material: meeting transcript without extra replays.
Limitations and Technical Details
The service works in a browser, registration is not required. Export is possible only in TXT format; DOCX and PDF formats are not available. There is no auto-save, integrations, or email delivery — the result must be saved independently.
The model can make mistakes, and the queue and availability of the service may change. This is worth considering if you work on a strict schedule. Do not rely on the service as the sole source of the protocol.
The service does not generate images, does not work with sound, and does not create video. It is a text tool for analyzing already ready text. Everything related to recording and transcription remains beyond its capabilities.
If the service is temporarily unavailable, save the text and try later. Do not paste confidential data if it contradicts your rules.
Meeting Summary is a helper tool for primary text analysis. It saves time on sorting but does not replace human verification. Delegate it routine classification, and leave responsibility for formulations, deadlines, and decisions to yourself. Such an approach gives a predictable result and reduces the risk of errors in the protocol.
Open tool: Meeting summary ↗Interface translations are machine-assisted and may need refinement.