DIALOGUE LEARNING DEVICE, SUMMARIZATION DEVICE, DIALOGUE LEARNING METHOD, SUMMARIZATION METHOD, PROGRAM

摘要

PROBLEM TO BE SOLVED: To reduce cost for building a summarization device and improve the accuracy of summarization.

SOLUTION: A dialogue learning device learns a hidden Markov model having a state to output the speech of a speaker for each domain by using a domain label indicating N number of dialogs with topic labels attached to each speech contained in each dialog and indicating to which of K kinds of domains N dialogues where speeches in each dialogue is provided with a topic label and each dialogue corresponds, ergodically connects all the states of the hidden Markov model and creates the hidden Markov model. A summarization device comprises: a feature amount extraction section; a topic label application section; a domain estimation section; and a selection section. The topic label application section estimates for each speech a most likely topic from each word in the speech, which is provided to the speech as a topic label. The domain estimation section estimates a domain of each speech. The selection section selects speeches where dialogues and domains correspond to each other from the dialogues.

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