Latent Dirichlet Allocation (LDA) has been used to generate text corpora#R##N#   topics recently. However, not all the estimated topics are of equal#R##N#   importance or correspond to genuine themes of the domain. Some of the topics#R##N#   can be a collection of irrelevant words or represent insignificant themes.#R##N#   This paper proposed a topic-sensitive algorithm for multi-document#R##N#   summarization. This algorithm uses LDA model and weight linear combination#R##N#   strategy to identify significance topic which is used in sentence weight#R##N#   calculation. Each topic is measured by three different LDA criteria.#R##N#   Significance topic is evaluated by using weight linear combination to combine#R##N#   the multi-criteria. In addition to topic features, the proposed approach also#R##N#   considered some statistics features, such as term frequency, sentence#R##N#   position, sentence length, etc. It not only highlights the advantages of#R##N#   statistics features, but also cooperates with topic model. The experiments#R##N#   showed that the proposed algorithm achieves better performance than the other#R##N#   state-of-the-art algorithms on DUC2002 corpus.
['Na Liu', 'Di Tang', 'Ying Lu', 'Xiao-Jun Tang', 'Hai-Wen Wang']
Topic-sensitive multi-document summarization algorithm