SUMMARYIncreasing the number of closed-captioned television programs represents a social responsibility in the sense of providing information. In terms of the system to create closed-captioned television programs by hand, there is considerable hope that the time involved can be reduced and the burden on workers can be eased. The system the authors report on automates three processes in the creation of closed-captioned television programs: summarization, synchronization, and closed-captioned screen creation, yielding from an electronic manuscript closed-caption data applicable to current closed-captioned broadcasts. The authors created closed captions for 12 types of news programs and one documentary program, confirming that the process of creating a closed-captioned television program could be completed in three to six times the program length, excluding the process of creating the electronic manuscript and testing/editing. The authors demonstrate the validity of their system insofar as the time needed to create closed captions using their system was about 70% of the time needed to create closed captions by hand, excluding the process of testing and editing.

We describe an ongoing project whose primary aim is to establish the technology of producing closed captions for TV news programs efficiently using natural language processing and speech recognition techniques for the benefit of the hearing impaired in Japan. The project is supported by the Telecommunications Advancement Organisation of Japan with the help of the ministry of Posts and Telecommunications. We propose natural language and speech processing techniques should be used for efficient closed caption production of TV programs. They enable us to summarise TV news texts into captions automatically, and synchronise TV news texts with speech and video automatically. Then the captions are superimposed on the screen. We propose a combination of shallow methods for the summarisation. For all the sentences in the original text, an importance measure is computed based on key words in the text to determine which sentences are important. If some parts of the sentences are judged unimportant, they are shortened or deleted. We also propose keyword pair model for the synchronisation between text and speech.

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