Towards Generating Text Summaries for Entity Chains

Shruti Chhabra, Srikanta Bedathur

Abstract: Given a large knowledge graph, discovering meaningful relationships between a given pair of entities has gained a lot of attention in the recent times. Most existing algorithms focus their attention on identifying one or more structures –such as relationship chains or subgraphs– between the entities. The burden of interpreting these results, after combining with contextual information and description of relationships, lies with the user. In this paper, we present a framework that eases this burden by generating a textual summary which incorporates the context and description of individual (dyadic) relationships, and combines them to generate a ranked list of summaries. We develop a model that captures key properties of a well-written text, such as coherence and information content. We focus our attention on a special class of relationship structures, two-length entity chains, and show that the generated ranked list of summaries have 79% precision at rank-1. Our results demonstrate that the generated summaries are quite useful to users.