Generating Characteristic Summaries for Entity Descriptions

Graph-structured data describing entities and their properties has become a notable component of the Web. With the increasing size of data graphs, an entity is often associated with too many property values to be entirely shown to the user, thereby requiring a compact but characteristic summary to present its most distinguishing features. This paper aims to automatically generate such characteristic entity summaries for human users. To achieve it, we exploit the informativeness of property values by analyzing the data graph using information theory. To improve the utility of information carried by a summary, we learn it from a text corpus. To reduce the information redundancy of a summary, we perform logical reasoning and measure similarity with statistical support. We formalize the entity summarization problem considering these factors as combinatorial optimization problems to solve. Experiments based on a real data graph and hand-crafted gold standards show that our approach improves on two state-of-the-art approaches in F-measure by 20.63%–38.79%.