<p>With advances in graph computing, an increasing amount of real-world data is now stored in computers as graphs. However, the time and resource costs associated with directly processing large graphs are escalating. As a result, graph summarization techniques, which provide a concise representation of graphs, have been extensively studied. While current graph summarization methods are effective in reducing processing time and minimizing storage overhead, we observe that the hierarchical and overlapping structures prevalent in graphs are not adequately explored, and the resulting summaries lack clear meaning. In this study, we present a new graph summarization model that uses a linear iterative hierarchical clustering algorithm combined with a lexicon-based structure extraction algorithm to generate relational patterns with clear hierarchical and overlapping meanings. In addition, we use the Minimum Description Length (MDL) principle for summarization encoding to save storage space. Experiments show that our approach achieves superior compression ratios on most datasets compared to existing methods and is able to uncover a larger number of relational patterns.</p>