A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions
<|reference_start|>A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions: 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.<|reference_end|>
@article{wang2024a,
    title={A Survey on Extractive Knowledge Graph Summarization: Applications,
  Approaches, Evaluation, and Future Directions},
    author={Xiaxia Wang, Gong Cheng},
    journal={arXiv preprint arXiv:2402.12001},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12001},
    primaryClass={cs.AI cs.DB cs.IR cs.SI}
}