  Extracting structured information from videos is critical for numerous
downstream applications in the industry. In this paper, we define a significant
task of extracting hierarchical key information from visual texts on videos. To
fulfill this task, we decouple it into four subtasks and introduce two
implementation solutions called PipVKIE and UniVKIE. PipVKIE sequentially
completes the four subtasks in continuous stages, while UniVKIE is improved by
unifying all the subtasks into one backbone. Both PipVKIE and UniVKIE leverage
multimodal information from vision, text, and coordinates for feature
representation. Extensive experiments on one well-defined dataset demonstrate
that our solutions can achieve remarkable performance and efficient inference
speed.
