An Approach to Automatic Summarization by Integrating Latent Dirichlet Allocation in Conditional Random Field

In recent years,Latent Dirichlet Allocation(LDA) has been widely applied in the document clustering,the text classification,the text segmentation,and even the query based multi-document summarization without supervision.LDA is recognized for its great power in modeling a document in a semantic way.In this paper we propose a new superivised method for the extraction-based single document summarization by adding LDA of the document as new features into a CRF summarization system.We study the power of LDA and analyze its different effects by changing the number of topics.Our experiments show that,by adding LDA features,the result of traditional CRF summarization system can be impressively increased.