  In this work, we develop a prompting approach for incremental summarization
of task videos. We develop a sample-efficient few-shot approach for extracting
semantic concepts as an intermediate step. We leverage an existing model for
extracting the concepts from the images and extend it to videos and introduce a
clustering and querying approach for sample efficiency, motivated by the recent
advances in perceiver-based architectures. Our work provides further evidence
that an approach with richer input context with relevant entities and actions
from the videos and using these as prompts could enhance the summaries
generated by the model. We show the results on a relevant dataset and discuss
possible directions for the work.
