  With the continuous growth of large Knowledge Graphs (KGs), extractive KG
summarization becomes a trending task. Aiming at distilling a compact subgraph
with condensed information, it facilitates various downstream KG-based tasks.
In this survey paper, we are among the first to provide a systematic overview
of its applications and define a taxonomy for existing methods from its
interdisciplinary studies. Future directions are also laid out based on our
extensive and comparative review.
