  The growth of videos in our digital age and the users' limited time raise the
demand for processing untrimmed videos to produce shorter versions conveying
the same information. Despite the remarkable progress that summarization
methods have made, most of them can only select a few frames or skims, creating
visual gaps and breaking the video context. This paper presents a novel
weakly-supervised methodology based on a reinforcement learning formulation to
accelerate instructional videos using text. A novel joint reward function
guides our agent to select which frames to remove and reduce the input video to
a target length without creating gaps in the final video. We also propose the
Extended Visually-guided Document Attention Network (VDAN+), which can generate
a highly discriminative embedding space to represent both textual and visual
data. Our experiments show that our method achieves the best performance in
Precision, Recall, and F1 Score against the baselines while effectively
controlling the video's output length. Visit
https://www.verlab.dcc.ufmg.br/semantic-hyperlapse/tpami2022/ for code and
extra results.
