A New Probabilistic Model for Title Generation

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.

• Type: Journal
• Year: 2002
• Where: COLING
• Authors: Rong Jin, Alexander G. Hauptmann
• Keywords: COLING 2002, COLING 2008, Information Source, Natural Language, Title Generation