  In this paper, we present TAC-SUM, a novel and efficient training-free
approach for video summarization that addresses the limitations of existing
cluster-based models by incorporating temporal context. Our method partitions
the input video into temporally consecutive segments with clustering
information, enabling the injection of temporal awareness into the clustering
process, setting it apart from prior cluster-based summarization methods. The
resulting temporal-aware clusters are then utilized to compute the final
summary, using simple rules for keyframe selection and frame importance
scoring. Experimental results on the SumMe dataset demonstrate the
effectiveness of our proposed approach, outperforming existing unsupervised
methods and achieving comparable performance to state-of-the-art supervised
summarization techniques. Our source code is available for reference at
\url{https://github.com/hcmus-thesis-gulu/TAC-SUM}.
