Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
This addresses extractive multi-document summarization by improving list prediction under constraints, though it appears incremental as it adapts existing models.
The paper tackles the problem of predicting sets or lists under knapsack constraints using submodular reward functions for quality and diversity, adapting sequence prediction models to imitate greedy maximization. Experiments on multi-document summarization show it outperforms state-of-the-art methods.
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.