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The rapid increase in the amount of published visual data and the limitedtime of users bring the demand for processing untrimmed videos to produceshorter versions that convey the same information. Despite the remarkableprogress that has been made by summarization methods, most of them can onlyselect a few frames or skims, which creates visual gaps and breaks the videocontext. In this paper, we present a novel methodology based on a reinforcementlearning formulation to accelerate instructional videos. Our approach canadaptively select frames that are not relevant to convey the informationwithout creating gaps in the final video. Our agent is textually and visuallyoriented to select which frames to remove to shrink the input video.Additionally, we propose a novel network, called Visually-guided DocumentAttention Network (VDAN), able to generate a highly discriminative embeddingspace to represent both textual and visual data. Our experiments show that ourmethod achieves the best performance in terms of F1 Score and coverage at thevideo segment level.

Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual Data