Towards generating text summaries for entity chains

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. © 2014 Springer International Publishing Switzerland.