  In this paper, we propose an integrated framework for multi-granular
explanation of video summarization. This framework integrates methods for
producing explanations both at the fragment level (indicating which video
fragments influenced the most the decisions of the summarizer) and the more
fine-grained visual object level (highlighting which visual objects were the
most influential for the summarizer). To build this framework, we extend our
previous work on this field, by investigating the use of a model-agnostic,
perturbation-based approach for fragment-level explanation of the video
summarization results, and introducing a new method that combines the results
of video panoptic segmentation with an adaptation of a perturbation-based
explanation approach to produce object-level explanations. The performance of
the developed framework is evaluated using a state-of-the-art summarization
method and two datasets for benchmarking video summarization. The findings of
the conducted quantitative and qualitative evaluations demonstrate the ability
of our framework to spot the most and least influential fragments and visual
objects of the video for the summarizer, and to provide a comprehensive set of
visual-based explanations about the output of the summarization process.
