01585nas a2200145   4500008004100000245004900041210004700090520110000137100002101237700002801258700003201286700001601318700001601334856008901350       2017                        eng d00aRelatedness-based Multi-Entity Summarization0 aRelatednessbased MultiEntity Summarization3 a<p>Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-ofthe-art entity summarization approaches.</p>
1 aGunaratna, Kalpa1 aYazdavar, Amir, Hossein1 aThirunarayan, Krishnaprasad1 aSheth, Amit1 aCheng, Gong  uhttps://daselab.cs.ksu.edu/publications/relatedness-based-multi-entity-summarization