Google’s Knowledge Graph offers structured summaries for entity searches. This provides a better user experience by focusing on the main aspects of the query entity only. But to do this Google relies on curated knowledge bases. In consequence, only entities included in such knowledge bases can benefit from such a feature. In this paper, we propose ARES, a system that automatically discovers a manageable number of attributes well-suited for high precision entity summarization. With any entity-centric query and exploiting diverse facts from Web documents, ARES derives a common structure (or schema) comprising attributes typical for entities of the same or similar entity type. To do this, we extend the concept of typicality from cognitive psychology and define a practical measure for attribute typicality. We evaluate the quality of derived structures for various entities and entity types in terms of precision and recall. ARES achieves results superior to Google’s Knowledge Graph or to frequency-based statistical approaches for structure extraction.