Does Video Summarization Require Videos? Quantifying the Effectiveness
  of Language in Video Summarization
Yoonsoo Nam Adam Lehavi Daniel Yang Digbalay Bose Swabha Swayamdipta Shrikanth Narayanan
Video summarization remains a huge challenge in computer vision due to the
size of the input videos to be summarized. We propose an efficient,
language-only video summarizer that achieves competitive accuracy with high
data efficiency. Using only textual captions obtained via a zero-shot approach,
we train a language transformer model and forego image representations. This
method allows us to perform filtration amongst the representative text vectors
and condense the sequence. With our approach, we gain explainability with
natural language that comes easily for human interpretation and textual
summaries of the videos. An ablation study that focuses on modality and data
compression shows that leveraging text modality only effectively reduces input
data processing while retaining comparable results.