Video Summarization with Neural Networks: A Systematic Comparison of State-of-the-Art Techniques

Video summarization represents an essential research field dedicated to developing fast methods that extract valuable content from extensive video collections. An evaluation of video summarization strategies with Neural Networks analyzes methodologies together with architectures and evaluation methods, datasets, and performance assessments. This paper conducts an in-depth analysis of different methodological approaches, including supervised and unsupervised learning, reinforcement learning, hybrid models, and object-centric methods, while assessing their performance traits and existing constraints. The popularity of deep learning techniques such as attention mechanism transformers along with hierarchical reinforcement learning shows continuous growth due to their effectiveness in improving summarization accuracy and efficiency. Summaries achieve higher quality through visual, audio, and textual features, which help produce better outputs for video tutorials combined with tournament highlights and security footage monitoring. The research investigates evaluation frameworks specifically by highlighting weaknesses in existing benchmark metrics and calling for Performance over Random (PoR) as a strong alternative framework. The current model faces ongoing issues with real-time operation, computational speed, and summation generation control based on user needs. The paper explores existing state-of-the-art approaches and proposes research directions focusing on scalability while developing user-customized frameworks and better-assessing metrics.