Table summarization can be of great help, which generates a concise and informative overview of a table to assist users to understand the table easily and unambiguously. A high-quality summary needs to have two desirable properties: presenting notable entities in the table and achieving broad coverage and high diversity on domains. However, notability and domain are often neglected in table summarization. Thus in this paper, we present a framework of domain-aware table summarization that is able to: (1) identify notable entities using a popularity-sensitive notability evaluation algorithm, (2) find core domains with a measurement of domain centrality, (3) and output the final high-quality summary using a three-phase clustering based algorithm. The experimental results show that our summarization method outperforms state-of-the-art methods by 9.62%, 2.78% and 6.77% on metrics coverage, diversity, and notability, respectively. We also conduct a user study to demonstrate that people can improve the accuracy of understanding tables by 17% with the help of our summarization technique.