Efficient organization and retrieval of news video content remain challenging due to the unstructured, complex nature of video data. Automated systems that accurately segment news videos into meaningful components are vital for media archiving, personalized content delivery, and intelligent search. A recent study tackles these challenges by comparing various deep learning classifiers designed to automate news video segmentation. It focuses on classifying five typical segment types in news broadcasts: advertisements, news stories, studio scenes, transitions, and visualizations. Accurate segmentation of these elements improves management and accessibility of news archives. The study developed and evaluated several state-of-the-art deep learning methods, including image-based and temporal models such as ResNet, ViViT, Audio Spectrogram Transformer (AST), and multimodal architectures combining different modalities. Training and evaluation utilized a carefully annotated dataset of 41 news videos, segmented into 1, 832 scene clips, each labeled according to the five segment classes, providing a robust basis for algorithm assessment. The classifiers were benchmarked on accuracy, computational efficiency, and real-world applicability. Key findings revealed that image-based classifiers, especially ResNet, outperformed more complex temporal models in classification accuracy, achieving an overall accuracy of 84. 34%.

This surpassed models like ViViT, which incorporate temporal data but require greater computational resources. ResNet’s strong performance, along with its lower resource demands, makes it practical for large-scale media processing. Notably, binary classification tasks for transitions and advertisements reached high accuracies of 94. 23% and 92. 74%, respectively, emphasizing the value of specialized classifiers for tasks like commercial detection and content summarization. The research offers important insights into deep learning architectures for news video segmentation. While temporal models theoretically provide richer context through motion and sequence information, the study shows single-frame image classifiers can achieve comparable or superior performance with less complexity—an important factor for scalable, efficient automated content organization. Practically, these findings benefit the media industry by enabling improved archiving through organized video repositories, facilitating personalized content delivery by extracting relevant segments tailored to users, and supporting intelligent video search that quickly locates specific content within vast news archives. In conclusion, the study demonstrates the viability of image-based deep learning classifiers, particularly ResNet, for effective news video segmentation. Their high accuracy and efficient resource use offer promising solutions for automated content organization in media applications. This work lays the foundation for future research exploring multimodal methods and classifier fine-tuning to further enhance the performance and flexibility of news video segmentation technology.