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Automatic title generation for spoken documents is believed to be an important key for browsing and navigation over huge quantities of multimedia content. A new framework of automatic title generation for Chinese spoken documents is proposed in this paper using a delicate scored Viterbi algorithm performed over automatically generated text summaries of the testing spoken documents. The Viterbi beam search is guided by a delicate score evaluated from three sets of models: term selection model tells the most suitable terms to be included in the title, term ordering model gives the best ordering of the terms to make the title readable, and title length model tells the reasonable length of the title. The models are trained from a training corpus which is not required to be matched with the testing spoken documents. Both objective evaluation based on F1 measure and subjective human evaluation for relevance and readability indicated the approach is very attractive.