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YouTube users looking for instructions for a specific task may spend a longtime browsing content trying to find the right video that matches their needs.Creating a visual summary (abridged version of a video) provides viewers with aquick overview and massively reduces search time. In this work, we focus onsummarizing instructional videos, an under-explored area of videosummarization. In comparison to generic videos, instructional videos can beparsed into semantically meaningful segments that correspond to important stepsof the demonstrated task. Existing video summarization datasets rely on manualframe-level annotations, making them subjective and limited in size. Toovercome this, we first automatically generate pseudo summaries for a corpus ofinstructional videos by exploiting two key assumptions: (i) relevant steps arelikely to appear in multiple videos of the same task (Task Relevance), and (ii)they are more likely to be described by the demonstrator verbally (Cross-ModalSaliency). We propose an instructional video summarization network thatcombines a context-aware temporal video encoder and a segment scoringtransformer. Using pseudo summaries as weak supervision, our network constructsa visual summary for an instructional video given only video and transcribedspeech. To evaluate our model, we collect a high-quality test set, WikiHowSummaries, by scraping WikiHow articles that contain video demonstrations andvisual depictions of steps allowing us to obtain the ground-truth summaries. Weoutperform several baselines and a state-of-the-art video summarization modelon this new benchmark.

TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency