Automated Minutes of Meeting Using a Multimodal Approach

Abstract

Abstract: The automated minutes of meeting using multimodal approach technique has emerged as a promising solution to lighten the time consuming and error prone manual process of capturing and summerize the meeting discussion. This research paper presents a novel approach for automating the minute of meeting through multimodal approach. The natural language processing is used to identify the different type of topic such as key topics, important discussion and other significant details discussed during meeting. The machine learning models are trained on datasets to classify and extract the relevant information accurately. Further, the research explores the use of advanced machine modals, such as whisper and transformers, to capture the context and refinement of meeting. These models enhance the accuracy and fastest of generated minutes of meeting. The assessment of the automated minutes of meeting generation involves compare of the outputs against the manually generated minutes of meeting by human notetakers. Metrics such as accuracy and F1 score are used to assess the system performance, ensuring the accuracy and quality of generated minute of meeting. This demonstrate that the automated minute of meeting using multimodal approach offers significant time savings, reduces human error, and increase overall efficiency in capturing and summarize the meeting discussion. The system shows promising potential for adoption in various organization and industry. In conclusion, this research paper present a comprehensive study about the automated generation of minute of meeting using multimodal approach. The proposed approach uses thee NLP techniques and advanced machine learning models to accurately extract and summarize meeting content. The results highlight the potential of this automated system to streamline meeting processes and enhances overall productivity in organization.