AI-Powered Based Automated Meeting Note Generation using a Gated Convolutional Neural Network approach in Large-Scale Video Platform
Abstract
Meeting note generation has become an essential factor in successful collaboration and knowledge sharing, especially with the fast rise in popular large-scale video conferencing platforms. Manual summarization can be both time-intensive and imprecise, requiring intelligent automated methods. The traditional summarization techniques are unsuitable to extract heterogeneous information in meeting transcripts and stay within contextual accuracy. A strong AI-based model that combines semantic and acoustic characters in order to produce accurate note generation is required in this paper. The paper provides a Gated Convolutional Neural Network (GCNN) based AI-driven model that is utilized to generate meeting notes in large-scale video platforms. The proposed system works with the meeting transcripts by utilizing preprocessing with noise reduction and standard scaling, and hybrid feature extraction based on TF-IDF and MFCC. The proposed GCNN model incorporates gating mechanisms to retain the significant features by eliminating the redundancy hence enhancing the contextual understanding. The performance of the proposed model was evaluated using accuracy of 96%, precision of 95%, Recall of 94% and F1-score of 94%, and the results were archived for further analysis. It has been demonstrated that the model strongly improves the quality, coherence, and accuracy of automated summaries, which makes it very effective in real-world collaborative settings.
Article Information
Journal |
International Journal of Future Innovative Science and Technology (IJFIST) |
|---|---|
Volume (Issue) |
Vol. 9 No. 1 (2026): International Journal of Future Innovative Science and Technology (IJFIST) |
DOI |
|
Pages |
114-123 |
Published |
February 24, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Uday Kiran Reddy Lingala, Kranthi Pakala (2026). AI-Powered Based Automated Meeting Note Generation using a Gated Convolutional Neural Network approach in Large-Scale Video Platform. International Journal of Future Innovative Science and Technology (IJFIST) , Vol. 9 No. 1 (2026): International Journal of Future Innovative Science and Technology (IJFIST) , pp. 114-123. https://doi.org/10.15662/IJFIST.2026.0901013 |
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