Abstract People sometimes choose word-like abbreviations to refer to items with a long description. These abbreviations usually come from the descriptive text of the item and are easy to remember and pronounce, while preserving the key idea of the item. Coming up with a nice abbreviation is not an easy job, even for human. Previous assistant naming systems compose names by applying hand-written rules, which may not perform well. In this paper, we propose to view the naming task as an artificial intelligence problem and create a data set in the domain of academic naming. To generate more delicate names, we propose a three-step framework, including description analysis, candidate generation and abbreviation ranking, each of which is parameterized and optimizable. We conduct experiments to compare different settings of our framework with several analysis approaches from different perspectives. Compared to online or baseline systems, our framework could achieve the best results.