Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization
Machine Learning
2014-03-18 v2
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.
Cite
@article{arxiv.1308.3541,
title = {Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization},
author = {Jiaji Zhou and Stephane Ross and Yisong Yue and Debadeepta Dey and J. Andrew Bagnell},
journal= {arXiv preprint arXiv:1308.3541},
year = {2014}
}
Comments
8 pages, ICML 2013 Workshop on Inferning: Interactions between Inference and Learning