Key points are not available for this paper at this time.
This paper introduces an advanced automated information management system, addressing the critical need for effective transcription and summarization in the face of the burgeoning volume of video data. The study's objective is to overcome the limitations of existing methods, primarily in handling vast video transcriptions and enhancing information retrieval. We propose an innovative integration of Mozilla Deep Speech, LangChain, VectorDB, and Large Language Models (LLMs), aiming to significantly improve the efficiency and accuracy of document-oriented processes in various sectors. Our approach leverages generative AI to transform the way organizations process video content, offering a solution to the lengthy and often challenging transcription tasks that current technologies struggle with. We highlight the potential economic impact, as underscored by a McKinsey study, indicating a substantial contribution of these technologies to various industries, particularly in customer service, sales, marketing, and software development. The methodology encompasses a comprehensive system architecture, utilizing NLP techniques and models like BERT, GPT, and spaCy, addressing the shortcomings of existing approaches with enhanced precision and adaptability. Initial findings demonstrate a transformative improvement in video summarization, providing significant benefits in education, journalism, and accessibility for the hearing impaired. This research not only offers a novel solution to existing challenges in data management but also sets a new standard for the application of generative AI and LLMs in automated information processing.
Lavanya et al. (Thu,) studied this question.