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A New Probabilistic Model for Title Generation

Rong Jin,Alexander Hauptmann

Published 2002 in International Conference on Computational Linguistics

ABSTRACT

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.

PUBLICATION RECORD

  • Publication year

    2002

  • Venue

    International Conference on Computational Linguistics

  • Publication date

    2002-08-24

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.3115/1072228.1072365
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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