@inproceedings{pouriyeh-etal-2017-es,
    title = "{ES}-{LDA}: Entity Summarization using Knowledge-based Topic Modeling",
    author = "Pouriyeh, Seyedamin  and
      Allahyari, Mehdi  and
      Kochut, Krzysztof  and
      Cheng, Gong  and
      Arabnia, Hamid Reza",
    editor = "Kondrak, Greg  and
      Watanabe, Taro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/landing_page/I17-1032/",
    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."
}