@InProceedings{pouriyeh-EtAl:2017:I17-1,
  author    = {Pouriyeh, Seyedamin  and  Allahyari, Mehdi  and  Kochut, Krzysztof  and  Cheng, Gong  and  Arabnia, Hamid Reza},
  title     = {ES-LDA: Entity Summarization using Knowledge-based Topic Modeling},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {316--325},
  abstract  = {With the advent of the Internet, the amount of Semantic Web documents that
	describe real-world entities and their inter-links as a set of statements have
	grown considerably. These descriptions are usually lengthy, which makes the
	utilization of the underlying entities a difficult task. Entity summarization,
	which aims to create summaries for real-world entities, has gained increasing
	attention in recent years. In this paper, we propose a probabilistic topic
	model, ES-LDA, that combines prior knowledge with statistical learning
	techniques within a single framework to create more reliable and representative
	summaries for entities. We demonstrate the effectiveness of our approach by
	conducting extensive experiments and show that our model outperforms the
	state-of-the-art techniques and enhances the quality of the entity summaries.},
  url       = {http://www.aclweb.org/anthology/I17-1032}
}