Smart Cloud-Based Meeting summarization using an Artificial Neural Network

The rapid adoption of video conference systems has created a demand to automate the meeting summary process, aiming to boost productivity and reduce memory consumption. This paper presents a meeting summarization framework based on an Artificial Neural Network (ANN) that can develop concise and rich context summaries for real-time or recorded meetings. The collection of data is carried out with the help of audio, video, and text reports from a public dataset and live meetings of based cloud database. Preprocessing involves converting speech into text, filtering out noise, performing speaker diarization, and removing irrelevant content store in the cloud of the network. The Natural Language Processing (NLP) technique of feature extraction employs TF-IDF, word embeddings, and other topic modelling to capture semantics in the cloud of the network in performance. An ANN is used to classify and prioritize important discussion points, decisions, and action items in the cloud. The measures of performance, being ROUGE score 83%, precision 77%, recall 77%, and F1-score 85%, are used. The experimental evidence indicates that the ANN model offers high summarization quality and relevance, allowing for efficient review of meetings and avoiding the need to spend time writing notes.