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Video summarization creates an abridged version (i.e., a summary) thatprovides a quick overview of the video while retaining pertinent information.In this work, we focus on summarizing instructional videos and propose a methodfor breaking down a video into meaningful segments, each corresponding toessential steps in the video. We propose \textbf{HierSum}, a hierarchicalapproach that integrates fine-grained local cues from subtitles with globalcontextual information provided by video-level instructions. Our approachutilizes the ``most replayed" statistic as a supervisory signal to identifycritical segments, thereby improving the effectiveness of the summary. Weevaluate on benchmark datasets such as TVSum, BLiSS, Mr.HiSum, and the WikiHowtest set, and show that HierSum consistently outperforms existing methods inkey metrics such as F1-score and rank correlation. We also curate a newmulti-modal dataset using WikiHow and EHow videos and associated articlescontaining step-by-step instructions. Through extensive ablation studies, wedemonstrate that training on this dataset significantly enhances summarizationon the target datasets.

HierSum: A Global and Local Attention Mechanism for Video Summarization