Paper: Large-Margin Learning of Submodular Summarization Models

ACL ID E12-1023
Title Large-Margin Learning of Submodular Summarization Models
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012

In this paper, we present a supervised learning approach to training submodu- lar scoring functions for extractive multi- document summarization. By taking a structured prediction approach, we pro- vide a large-margin method that directly optimizes a convex relaxation of the de- sired performance measure. The learning method applies to all submodular summa- rization methods, and we demonstrate its effectiveness for both pairwise as well as coverage-based scoring functions on mul- tiple datasets. Compared to state-of-the- art functions that were tuned manually, our method significantly improves performance and enables high-fidelity models with num- ber of parameters well beyond what could reasonably be tuned by hand.