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

Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell

May 01, 2013 (modified: May 01, 2013) ICML 2013 Inferning submission readers: everyone
  • Decision: conferencePoster
  • 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 approach , by a reduction to online learning, we show how to adapt two sequence prediction models to imitate greedy maximization under knapsack constraint problems: CONSEQOPT and SCP. Experiments on extractive multi-document summarization show that our approach outperforms existing state-of-the-art methods.

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