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Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization

Jiaji Zhou,S. Ross,Yisong Yue,Debadeepta Dey,J. Bagnell

Published 2013 in arXiv.org

ABSTRACT

We study the problem of predicting a set or list of options under knapsack constraint. The quality of such lists are evaluated by a submodular reward function that measures both quality and diversity. Similar to DAgger (Ross et al., 2010), by a reduction to online learning, we show how to adapt two sequence prediction models to imitate greedy maximization under knapsack constraint problems: CONSEQOPT (Dey et al., 2012) and SCP (Ross et al., 2013). Experiments on extractive multi-document summarization show that our approach outperforms existing state-of-the-art methods.

PUBLICATION RECORD

  • Publication year

    2013

  • Venue

    arXiv.org

  • Publication date

    2013-08-15

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1308.3541
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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