  With the tremendously increasing number of videos, there is a great demand
for techniques that help people quickly navigate to the video segments they are
interested in. However, current works on video understanding mainly focus on
video content summarization, while little effort has been made to explore the
structure of a video. Inspired by textual outline generation, we introduce a
novel video understanding task, namely video outline generation (VOG). This
task is defined to contain two sub-tasks: (1) first segmenting the video
according to the content structure and then (2) generating a heading for each
segment. To learn and evaluate VOG, we annotate a 10k+ dataset, called DuVOG.
Specifically, we use OCR tools to recognize subtitles of videos. Then
annotators are asked to divide subtitles into chapters and title each chapter.
In videos, highlighted text tends to be the headline since it is more likely to
attract attention. Therefore we propose a Visual Subtitle feature Enhanced
video outline generation model (VSENet) which takes as input the textual
subtitles together with their visual font sizes and positions. We consider the
VOG task as a sequence tagging problem that extracts spans where the headings
are located and then rewrites them to form the final outlines. Furthermore,
based on the similarity between video outlines and textual outlines, we use a
large number of articles with chapter headings to pretrain our model.
Experiments on DuVOG show that our model largely outperforms other baseline
methods, achieving 77.1 of F1-score for the video segmentation level and 85.0
of ROUGE-L_F0.5 for the headline generation level.
