• Dr.Sandeep Bhat
• Shreya Shetty
• Abhishek S Thayyil
• Karthik Ajay
• Namit Jagadeesh,Dev Krishna

In an era of burgeoning multimedia content, efficient video summarization has become crucial for quick content review and information retrieval. This abstract presents a novel video summarization framework that integrates advanced deep learning techniques with multi-modal analysis to generate concise yet comprehensive video summaries. The system leverages convolutional neural networks for visual feature extraction, recurrent neural networks to capture temporal dynamics, and natural language processing models to incorporate semantic context from any available audio or textual metadata. Through a sophisticated fusion of these modalities, the summarizer identifies key frames and segments that encapsulate the essential narrative and informational content of the original video. Experimental results on benchmark datasets demonstrate that our approach not only effectively reduces video length but also preserves critical semantic cues, thereby enhancing user accessibility and engagement. This work lays the foundation for future advancements in real-time summarization, adaptive content customization, and cross-domain applications ranging from educational content curation to surveillance and entertainment.

"VIDEO SUMMARIZER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.300-305, July-2025, Available :http://www.jetir.org/papers/JETIRGX06055.pdf

"VIDEO SUMMARIZER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp300-305, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06055.pdf