In the fast-paced professional world, effective meeting documentation is essential but often time-consuming. This publication presents a lightweight AI-powered web application that summarizes lengthy meeting transcripts into concise summaries and actionable points. Using a minimal Flask backend and OpenAI’s GPT model, the tool transforms unstructured dialogue into clear, structured minutes within seconds. The project aims to improve productivity and eliminate the manual effort involved in creating post-meeting documentation, making it ideal for educators, managers, and team leads.
Meetings are an integral part of collaboration across industries, yet the task of manually summarizing discussions and documenting follow-ups often leads to errors, delays, or loss of information. This project addresses the need for a simple, accessible tool that converts raw transcripts into meaningful outputs. By integrating OpenAI’s language model into a clean, web-based interface, users can effortlessly generate summaries and action items from their meeting content—without needing technical expertise or elaborate software.
Several commercial tools offer transcription and summarization services—such as Otter.ai, Fireflies.ai, and Google Meet summaries. However, these platforms often require subscriptions, complex setups, or integrations with calendars and third-party apps. In contrast, this summarizer stands out by being:
This makes it ideal for solo professionals, educators, and small teams looking for on-demand summarization without ecosystem lock-in.
The system architecture is deliberately simple:
The key to performance lies in carefully designed prompt engineering and ensuring that the GPT API returns consistent and structured outputs, regardless of input length or tone.
To validate the summarizer's effectiveness, transcripts of varying length, tone, and structure were tested:
Prompts were fine-tuned to ensure:
Qualitative analysis showed that the tool was accurate in identifying the core discussion points and often extracted action items that matched those taken manually by participants.
The summarizer performed reliably across input types. Example results include:
Input (excerpt):
“Okay, let’s finalize the parent-teacher meeting for Friday. Arun will handle the invites. I’ll speak to the principal. Let's also remind parents to submit the feedback forms.”
Output Summary:
"The team scheduled the parent-teacher meeting for Friday. Responsibilities were delegated for communication and coordination."
Action Items:
In multiple test runs, the system produced accurate and grammatically correct outputs with a human-like tone.
While the application performs well, some limitations were noted:
Future enhancements could include:
Despite its simplicity, the tool demonstrated high utility in both professional and academic scenarios.
This project successfully demonstrates how generative AI can assist in automating time-consuming yet critical tasks like meeting summarization. The tool’s minimalist design makes it easy to use and integrate into daily workflows without dependency on external platforms. With clear scope for future improvements, the Meeting Minutes Summarizer lays the groundwork for more advanced, AI-driven documentation tools across education and industry.