@InProceedings{kim-choi:2016:COLINGDEMO,
  author    = {Kim, Eun-kyung  and  CHOI, KEY-SUN},
  title     = {MAGES: A Multilingual Angle-integrated Grouping-based Entity Summarization System},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {203--207},
  abstract  = {This demo presents MAGES (multilingual angle-integrated grouping-based entity
	summarization), an entity summarization system for a large knowledge base such
	as DBpedia based on a entity-group-bound ranking in a single integrated entity
	space across multiple language-specific editions. MAGES offers a multilingual
	angle-integrated space model, which has the advantage of overcoming missing
	semantic tags (i.e., categories) caused by biases in different language
	communities, and can contribute to the creation of entity groups that are
	well-formed and more stable than the monolingual condition within it. MAGES can
	help people quickly identify the essential points of the entities when they
	search or browse a large volume of entity-centric data. Evaluation results on
	the same experimental data demonstrate that our system produces a better
	summary compared with other representative DBpedia entity summarization
	methods.},
  url       = {http://aclweb.org/anthology/C16-2043}
}