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Automatic summarization is developed to extract the representative contents or sentences from a large corpus of documents. This paper presents a new hierarchical representation of words, sentences and documents in a corpus, and infers the Dirichlet distributions for latent topics and latent themes in word level and sentence level, respectively. The sentence-based latent Dirichlet allocation (SLDA) is accordingly established for document summarization. Different from the vector spacedoi:10.1109/icassp.2009.4959927 dblp:conf/icassp/ChangC09 fatcat:i7iflhvfonbrrcwof5sbj7d6wq