  The rapid increase in the amount of published visual data and the limited
time of users bring the demand for processing untrimmed videos to produce
shorter versions that convey the same information. Despite the remarkable
progress that has been made by summarization methods, most of them can only
select a few frames or skims, which creates visual gaps and breaks the video
context. In this paper, we present a novel methodology based on a reinforcement
learning formulation to accelerate instructional videos. Our approach can
adaptively select frames that are not relevant to convey the information
without creating gaps in the final video. Our agent is textually and visually
oriented to select which frames to remove to shrink the input video.
Additionally, we propose a novel network, called Visually-guided Document
Attention Network (VDAN), able to generate a highly discriminative embedding
space to represent both textual and visual data. Our experiments show that our
method achieves the best performance in terms of F1 Score and coverage at the
video segment level.
