Selective compositionality – the ability to select relevant concepts from the acquired knowledge and composing them into a coherent
linguistic form – is one of the cornerstones of human language. In this work, we study a specific form of selective compositionality where the
goal is to synthesize a succinct textual description of an entity given the available factual knowledge. Large-scale open-domain knowledge
graphs such as Wikidata and Google Knowledge Graph sparsely provide a synoptic textual description that entails the most discerning facts of an entity (e.g. Roger Federer: Swiss tennis player). Such descriptions effectively provide for a near-instantaneous human understanding of an entity and are also useful in a number of linguistic tasks, including named entity disambiguation. Additionally, they can serve as
fine-grained ontological types in question answering and reasoning-driven applications. Unfortunately, due to the rapid increase in the number of entities, such descriptions are missing for many entities in the existing knowledge graphs, necessitating an automated
synthesis of succinct textual descriptions from underlying factual knowledge. To this end, we propose a neural architecture that can
precisely generate synoptic descriptions for a diverse set of entities. Through extensive evaluation, we demonstrate the ability of our
architecture to select relevant facts and composing them to coherent and succinct descriptions more accurately by pitting it against a number of competitive baselines.