Date of Original Version
Copyright 2013 by the author(s)
Abstract or Description
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.
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.
Proceedings of the ICML Workshop on Inferning, 2013.