Abstract: We describe efficient algorithms for accurately estimating the number of matches of a small node-labeled tree, i.e., a twig, in a large node-labeled tree, using a summary data structure. This problem is of interest for queries on XML and other hierarchical data, to provide query feedback and for cost-based query optimization. Our summary data structure scalably represents approximate frequency information about twiglets (i.e., small twigs) in the data tree. Given a twig query, the number of matches is estimated by creating a set of query twiglets, and combining two complementary approaches: Set Hashing, used to estimate the number of matches of each query twiglet, and Maximal Overlap, used to combine the query twiglet estimates into an estimate for the twig query. We propose several estimation algorithms that apply these approaches on query twiglets formed using variations on different twiglet decomposition techniques. We present an extensive experimental evaluation using several real XML data sets, with a variety of twig queries. Our results demonstrate that accurate and robust estimates can be achieved, even with limited space.