Summary of A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions, by Xiaxia Wang et al.
A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions
by Xiaxia Wang, Gong Cheng
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB); Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey paper provides an in-depth examination of extractive Knowledge Graph (KG) summarization, a rapidly growing field driven by the increasing size of large KGs. The goal is to condense complex information into a compact subgraph that facilitates various downstream tasks utilizing KG-based methods. The authors present a systematic overview of existing approaches and define a taxonomy for this interdisciplinary study, providing future directions based on their comprehensive review. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about summarizing big collections of information called Knowledge Graphs. Imagine trying to find the most important details in a huge encyclopedia – that’s what this task does! It helps make it easier to use KGs for things like searching or answering questions. The researchers looked at how different people have tried to solve this problem and came up with a way to organize their ideas into categories. They also suggested new directions for future research. |
Keywords
» Artificial intelligence » Knowledge graph » Summarization
