In the 21st century, the internet has become a ubiquitous platform for
exchanging information, contributing to an extreme daily influx of approximately 2.5
Exabytes of data. Statistics show that a substantial 1/5th of the data transmitted over the
internet comprises videos. With 30,000 hrs. of videos uploaded on YouTube every
hour, the need to evaluate and derive valuable insights from this massive amount of
video data becomes apparent in a dynamic landscape. Particularly for professionals
engaged in internet exploration for various purposes, such as prominent data analysts,
data scientists, and cybercrime analysts, it becomes apparent that they need to have an
understanding of it. Though traditional machine learning-based systems exist, they
have become obsolete due to modern times’ issues, including decreased performance,
greater computing complexity, and resource-intensive processes. GenAI has effectively
addressed these problems, which is considered a transformative force with enhanced
accuracy and semantic comprehension. This chapter focusses on the task of video
summarization, which has been poised to supplant antiquated methodologies with the
cutting-edge capabilities of GenAI. We have also applied the concepts of hybridization
and fine-tuning of models in machine learning to create an improved version of
generative models. The proposed methodology has demonstrated better performance in
obtaining information, understanding computational complexity, etc.
Keywords: Abstractive summarization, Extractive summarization, Generative AI, Video summarization.
