Simple Temporal Attention (STA) in video summarization can improve deep learning model performance while tackling complexity and multi-view dependency problems. Many of the current models are too complex and dependent on multi-view setups to be scalable in single-camera settings. The suggested STA mechanism reduces model complexity without sacrificing accuracy, making it easier to recognize important moments in videos. To further increase the efficacy of summarization, a spatio-temporal mechanism is also introduced to capture crucial dynamics between video frames. The approach is evaluated on two benchmark datasets, UCF50 and TVSum, demonstrating significant improvements in model performance. This study provides a scalable solution for video summarization by highlighting the useful advantages of integrating STA for producing succinct and informative video summaries through a comparison of different deep learning.