With the development of Semantic Web, entity summarization has become an
emerging task to generate concrete summaries for real world entities. To solve
this problem, we propose an approach named MPSUM that extends a probabilistic
topic model by integrating the idea of predicate-uniqueness and
object-importance for ranking triples. The approach aims at generating brief
but representative summaries for entities. We compare our approach with the
state-of-the-art methods using DBpedia and LinkedMDB datasets.The experimental
results show that our work improves the quality of entity summarization.

本文提出了一种名为 MPSUM 的方法来解决实体概括生成的问题，该方法使用概率主题模型并集成谓词唯一性和对象重要性的想法进行三元组排名，以生成简洁但代表性的实体概括。在使用 DBpedia 和 LinkedMDB 数据集进行对比实验后，结果表明我们的方法提高了实体概括的质量。

基于谓词匹配的实体摘要

MPSUM: Entity Summarization with Predicate-based Matching

Social media has now become the de facto information source on real world
events. The challenge, however, due to the high volume and velocity nature of
social media streams, is in how to follow all posts pertaining to a given event
over time, a task referred to as story detection. Moreover, there are often
several different stories pertaining to a given event, which we refer to as
sub-stories and the corresponding task of their automatic detection as
sub-story detection. This paper proposes hierarchical Dirichlet processes
(HDP), a probabilistic topic model, as an effective method for automatic
sub-story detection. HDP can learn sub-topics associated with sub-stories which
enables it to handle subtle variations in sub-stories. It is compared with
state- of-the-art story detection approaches based on locality sensitive
hashing and spectral clustering. We demonstrate the superior performance of HDP
for sub-story detection on real world Twitter data sets using various
evaluation measures. The ability of HDP to learn sub-topics helps it to recall
the sub- stories with high precision. Another contribution of this paper is in
demonstrating that the conversational structures within the Twitter stream can
be used to improve sub-story detection performance significantly.

本研究旨在使用基于概率主题模型的层次狄利克雷过程（HDP）方法进行社交媒体平台上的子事件检测，并对其性能与基于局部敏感哈希和谱聚类的现有方法进行比较，结果表明 HDP 具有更高的召回率和更高的精度。此外，研究还显示，在 Twitter 数据集上，会话结构也可以用于显著提高子事件检测的性能。