Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published March 27, 2019 | Submitted
Conference Paper Open

Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization

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.

Additional Information

© 2013 by the author(s). Presented at the International Conference on Machine Learning (ICML) workshop on Inferning: Interactions between Inference and Learning, Atlanta, Georgia, USA, 2013. This research was supported by NSF NRI Purposeful Prediction and the Intel Science and Technology Center on Embedded Computing. We gratefully thank Martial Hebert for valuable discussions and Alex Kulesza for providing data and code.

Attached Files

Submitted - 1308.3541.pdf

Files

1308.3541.pdf
Files (221.0 kB)
Name Size Download all
md5:d0c629722c12ba8afdb19666f91527c4
221.0 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 20, 2023