Generating Text Summaries of Graph Snippets

With the availability of large entity-relationship graphs, finding the best relationship between entities is a problem that has attracted a lot of attention. Given two or more entities, the goal of most algorithms is to produce a graph structure of varying complexity (i.e., a simple path, a minimal weighted tree, or a dense subgraph etc.) as a way of characterizing the relationship between given entities. However, no attention is paid to the interpretability of these results – i.e., the ability of humans to read these and comprehend the context in which these relationships exist. A key obstacle in this direction is the lack of necessary linguistic context and natural textual result formulations. We pursue the idea of using entity-centric summarization as a way of closing this gap. We aim to turn the resulting graph structures into one or more coherent textual snippets (or summaries) that can be easily read and interpreted. In this short position paper, we first outline two different scenarios that result in slightly different formulations of the problem. Based on preliminary experimental results, we discuss the challenges that are inherent in this setting.