Relatedness-based Multi-Entity Summarization

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-of-the-art entity summarization approaches.

Authors

• Kalpa Gunaratna
• Amir Hossein Yazdavar
• Krishnaprasad Thirunarayan
• Amit Sheth University of South Carolina
• Gong Cheng Nanjing University

Keywords

• Knowledge Representation, Reasoning, and Logic: Knowledge Representation Languages
• Natural Language Processing: Information Retrieval