  In this paper, we present a supervised learning approach to training
submodular scoring functions for extractive multi-document summarization. By
taking a structured predicition approach, we provide a large-margin method that
directly optimizes a convex relaxation of the desired performance measure. The
learning method applies to all submodular summarization methods, and we
demonstrate its effectiveness for both pairwise as well as coverage-based
scoring functions on multiple datasets. Compared to state-of-the-art functions
that were tuned manually, our method significantly improves performance and
enables high-fidelity models with numbers of parameters well beyond what could
reasonbly be tuned by hand.
