- 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.