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A New Probabilistic Model for Title Generation

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journal contribution
posted on 01.12.2007 by Rong Jin, Alexander Hauptmann
Title generation is a complex task involving both natural language understanding and natural language synthesis. In this paper, we propose a new probabilistic model for title generation. Different from the previous statistical models for title generation, which treat title generation as a generation process that converts the ‘document representation’ of information directly into a ‘title representation’ of the same information, this model introduces a hidden state called ‘information source’ and divides title generation into two steps, namely the step of distilling the ‘information source’ from the observation of a document and the step of generating a title from the estimated ‘information source’. In our experiment, the new probabilistic model outperforms the previous model for title generation in terms of both automatic evaluations and human judgments.

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01/12/2007

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