Data summarization aims at utilizing a small-scale summary to represent massive datasets as a whole, which is useful for visualization and information sipped generation. However, most existing studies of hierarchical summarization only work onone single treeby selecting$k$representative nodes, which neglects an important problem of comparative summarization on two trees. In this paper, given two trees with the same topology structure and different node weights, we aim at finding$k$representative nodes, where$k_{1}$nodes summarize the common relationship between them and$k_{2}$nodes highlight significantly different subtrees meanwhile satisfying$k_{1}+k_{2}=k$. To optimize summarization results, we introduce a scaling coefficient for balancing the summary view between two subtrees in terms of similarity and difference. Additionally, we propose a novel definition based on the Hellinger distance to quantify the node distribution difference between two subtrees. We present a greedy algorithm SVDT to find high-quality results with approximation guaranteed in an efficient way. Furthermore, we explore an extension of our comparative summarization to handle two trees with different structures. Extensive experiments demonstrate the effectiveness and efficiency of our SVDT algorithm against existing summarization competitors.