Abstract：Topic models have become important tools in machine learning and natural language processing, which can discover hidden topics in large-scale corpus. However, as the size of the corpus grows, the scale of discovered topics grows. Most topic models are on the basis of bag-of-words model, and the orders between terms cannot be described, which makes topics undistinguishable from each other. Ranking topic models without query framework is proposed in this paper, in which topics are ranked to get ordered topic list according to their relationships. Topic relationships are used to evaluate topic influence in topic level, and term significance is used to evaluate term importance in term level and popular ranking topics with little semantics are weakened. Since there is no acknowledged evaluation criterion in ranking topic model, ranked topics are used as features to perform automatic summarization of multi-document, and the performance of ranking topic models are indirectly measured by summarization performance. The experimental results show that ranking topic models outperform topic models without ranking.
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