  Annotation of multimedia data by humans is time-consuming and costly, while
reliable automatic generation of semantic metadata is a major challenge. We
propose a framework to extract semantic metadata from automatically generated
video captions. As metadata, we consider entities, the entities' properties,
relations between entities, and the video category. We employ two
state-of-the-art dense video captioning models with masked transformer (MT) and
parallel decoding (PVDC) to generate captions for videos of the ActivityNet
Captions dataset. Our experiments show that it is possible to extract entities,
their properties, relations between entities, and the video category from the
generated captions. We observe that the quality of the extracted information is
mainly influenced by the quality of the event localization in the video as well
as the performance of the event caption generation.
