Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sarkar, Sarnali; Lamani, Manjunath Ramanna; Vinodha, D. Title A name given to the resource Integrating Simple Temporal Attention for Improved Video Summarization Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1341 LNNS;pp.217-231 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5126-9_16" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5126-9_16</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020795828?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020795828?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sarkar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Kumbalgodu, Karnataka, Bangalore, India; Lamani M.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Kumbalgodu, Karnataka, Bangalore, India; Vinodha D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Kumbalgodu, Karnataka, Bangalore, India Description An account of the resource 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. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Deep learning; Model complexity; Scalability; Simple temporal attention (STA); Single-camera environments; TVSum; UCF50; Video summarization are some examples of spatiotemporal mechanisms Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965125-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online