Previously topic models such as PLSI (Probabilistic Latent Semantic Indexing) and LDA (Latent Dirichlet Allocation) were developed for modeling the contents of plain texts. Recently, topic models for processing hypertexts such as web pages were also proposed. The proposed hypertext models are generative models giving rise to both words and hyperlinks. This paper points out that to better represent the contents of hypertexts it is more essential to assume that the hyperlinks are fixed and to define the topic model as that of generating words only. The paper then proposes a new topic model for hypertext processing, referred to as Hypertext Topic Model (HTM). HTM defines the distribution of words in a document (i.e., the content of the document) as a mixture over latent topics in the document itself and latent topics in the documents which the document cites. The topics are further characterized as distributions of words, as in the conventional topic models. This paper further proposes a method for learning the HTM model. Experimental results show that HTM outperforms the baselines on topic discovery and document classification in three datasets.