  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 on \emph{one single tree} by 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 sub-trees meanwhile satisfying $k_1+k_2=k$. To optimize
summarization results, we introduce a scaling coefficient for balancing the
summary view between two sub-trees in terms of similarity and difference.
Additionally, we propose a novel definition based on the Hellinger distance to
quantify the node distribution difference between the sub-trees. 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.
