Summarization for Enhanced Video Conference Collaboration

Abstract

Abstract: Efficient collaboration during video conferences is crucial for team productivity. However, the absence of effective mechanisms to capture and preserve key insights often results in misunderstandings and reduced efficiency. The proposed initiative focuses on developing a web application to improve collaboration in video conferences by converting speech conversations into summarized text. Leveraging machine learning models, the system transcribes spoken content using whisper model and employs abstractive summarization using Pegasus X-Sum model. The Whisper architecture is implemented as an encoder-decoder Transformer. Input audio is split into 30-second chunks, converted into a log-Mel spectrogram, and then passed into an encoder. A decoder is trained to predict the corresponding text caption, intermixed with special tokens that direct the model to perform transcription. The goal is to empower users to effortlessly capture and retain crucial discussions, promoting better understanding and decision-making.