Automatic Labeling of Topic Models Using Graph-Based Ranking

Generated topic label, an alternative representation of topics learned by topic model, is widely used to help the user interpret the topics more efficiently. A major challenge now is to label a discovered topic accurately in an objective way. This article introduces a novel graph-based ranking model (TLRank), to find a meaningful topic label with high Relevance, Coverage, and Discrimination. The model applies a specific strategy that suppresses or enhances the matrix transition probability according to the textual similarity between vertices (sentences) and the characteristics of vertices respectively. Moreover, to boost diversity and enhance performance, TLRank scores the candidate sentences and refrains redundancy of topic labels simultaneously in a single labeling process. In our experiments, the evaluation results showed that the TLRank model significantly and consistently outperformed the prevailing state-of-the-art and classic models in topic labeling task.