  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.
