论文标题
mpsum：实体摘要，基于谓词匹配
MPSUM: Entity Summarization with Predicate-based Matching
论文作者
论文摘要
随着语义网络的发展，实体摘要已成为为现实世界实体生成具体摘要的新任务。为了解决这个问题，我们提出了一种名为MPSUM的方法，该方法通过整合谓词 - 唯一性和对象体现排名三倍的概念来扩展概率主题模型。该方法旨在为实体生成简短但代表性的摘要。我们将我们的方法与使用DBPEDIA和LinkedMDB数据集的最新方法进行了比较。实验结果表明，我们的工作改善了实体摘要的质量。
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.
