AI Meeting Summary Generator: How It Works and What to Use

An AI meeting summary generator takes a meeting transcript as input and produces a structured summary as output. The quality of the summary depends on the quality of the transcript, the sophistication of the underlying model, and how well the tool is configured to extract the right information for your use case. Understanding how each step works helps teams evaluate tools and improve their meeting practices.

How AI meeting summary generation works

• Step 1 — Transcription: the meeting audio is converted to text using automatic speech recognition (ASR). Speaker diarization labels each segment by who spoke.
• Step 2 — Segmentation: the transcript is divided into meaningful segments (topic changes, decision moments, action item commits).
• Step 3 — Extraction: the AI model identifies the key information: what was discussed, what was decided, and what actions were committed to.
• Step 4 — Structuring: the extracted information is formatted into a readable summary — typically a narrative paragraph, a decisions list, and an action items list.
• Step 5 — Delivery: the summary is sent to attendees, saved to a workspace, or pushed to a CRM.

What makes one summary generator better than another

• Transcript quality is the primary input — better ASR produces better summaries.
• Model sophistication affects whether the tool captures subtle decisions vs. only explicit statements.
• Meeting structure matters — clear agendas and explicit decisions produce better AI summaries.
• Delivery speed matters — a recap that arrives in minutes is more useful than one that arrives the next day.

MeetOye's Oya summary generator

Oya generates meeting summaries immediately when the meeting ends. The summary is structured as a narrative overview, a decisions list, and an action items list with speaker attribution. The recap email reaches every attendee before they open their next task. For teams that have tried standalone summary tools with inconsistent quality, the improvement usually comes from the underlying transcript quality — MeetOye's native audio pipeline produces cleaner transcripts than tools that process audio from a bot participant recording.