In the modern era, the sheer volume of video content available poses significant challenges for viewers trying to extract critical information swiftly. Addressing this issue, our research introduces an innovative Highlight Detection and Pairwise Ranking Model for video summarization. This paper delves into the design and evaluation of a hybrid model that effectively identifies and ranks video segments, producing concise and coherent video summaries. By integrating deep learning techniques for highlight detection with pairwise ranking, our model assesses not only the individual importance of each segment but also its relevance in relation to others. Through extensive experiments on diverse datasets, we demonstrate that our approach consistently outperforms existing state-of-the-art video summarization techniques in terms of both user satisfaction and objective measures. Our findings indicate a promising direction for the efficient summarization of long-form videos, catering to the evolving needs of contemporary audiences.