  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.
