# A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions

arXiv: https://arxiv.org/abs/2402.12001 · HTML: https://mlsanity.com/paper/2402.12001/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [Dataset search: a survey](https://mlsanity.com/paper/1901.00735/) (2019)
- [The Odyssey Approach for Optimizing Federated SPARQL Queries](https://mlsanity.com/paper/1705.06135/) (2017)
- [Entity Summarization: State of the Art and Future Challenges](https://mlsanity.com/paper/1910.08252/) (2019)
- [Enriching Documents with Compact, Representative, Relevant Knowledge Graphs](https://mlsanity.com/paper/2005.00153/) (2020)
- [Towards More Usable Dataset Search: From Query Characterization to Snippet Generation](https://mlsanity.com/paper/1908.11146/) (2019)

## Built on by (most cited first, 1 of 1)

- [COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs](https://mlsanity.com/paper/2605.14900/) (2026)

## Abstract (arXiv)

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.