AI-Based Meeting Minute Generator A Review Article

Ritu Kushwaha, Sneha, Naman Choudhary, Vansh Sharma and Kapil Dev Sharma

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 2, 907-913

Abstract: Meetings are essential for communication, teamwork, and decision-making in today’s organizations. However, manually documenting meeting minutes takes a lot of time and often leads to errors, resulting in incomplete or inaccurate records. With the rise of virtual meetings, there is an increasing need for automated solutions to capture and summarize discussions efficiently. This paper introduces an AI-Based Meeting Minute Generator, a smart system that automates the creation of structured meeting minutes from audio or text inputs. The system uses advanced Speech Recognition techniques to turn spoken language into text and applies Natural Language Processing (NLP) to extract key insights like summaries, important discussion points, and action items. It integrates transformer-based models for summarization, keyword extraction methods, and Named Entity Recognition (NER) to identify relevant entities. Additionally, the system includes security features like malware detection and supports processing large files, making it more efficient and scalable than existing tools. This solution boosts productivity, ensures accuracy, and cuts down on manual effort. It can effectively serve corporate meetings, educational settings, and online collaboration platforms.

Keywords: Meeting Minutes; Speech Recognition; Natural Language Processing; Text Summarization; AI; Automation (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST2613376 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST2613376/IJSRST2613376 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i2:id:1540

DOI: 10.32628/IJSRST2613376

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().