Abstract: Table semantic summarization is to create a small size summary table by using a few general tuples to replaceall tuples in the raw data table with the help of attributes' concept hierarchies. I}he aim of summarisation is to restrict the size of the summary table to a fixed value with the semantic information remained in it as more as possible. The existing method translates table summarization to a set covering problem and spends much cost in the problem translation which makes it impractical. We defined the metric space of tuples with multi attributes’hierarchical structure and translated this problem to a clustering problem in a hierarchial space. We proposed two algorithms. One was hierarchial agglomerative method and the other was based on the idea of adjusting the resolution of the hierarchial space. The experiment on real life dataset shows that our methods arc better than the existing one in both running time and summary quality.

Key words: Data generalization, Concept hierarchy, Semantic summarization, Hierarchical clustering