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arXiv · 1308.3541

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

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

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Jiaji Zhou, Stephane Ross, Yisong Yue, Debadeepta Dey, J. Andrew Bagnell. 2013-08-16. Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization. https://arxiv.org/abs/1308.3541

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