Relatedness-based Multi-Entity Summarization

TitleRelatedness-based Multi-Entity Summarization
Publication TypeConference Paper
Year of Publication2017
AuthorsKalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng
Conference NameIn IJCAI: proceedings of the conference (Vol. 2017, p. 1060)
VolumeVol. 2017
Date Published08/2017
Conference LocationSydney, Australia
Keywordsclustering, Entity Summarization, knowledge graph

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.

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