Large-Margin Learning of Submodular Summarization Methods

Details Large-Margin Learning of Submodular Summarization Methods Large-Margin Learning of Submodular Summarization Methods

2011-10-10

arxiv.org/abs/1110.2162v2

Computer Science

Artificial Intelligence

update: improved formatting (figure placement) and algorithm pseudocode clarity (Fig. 3)

Scientific paper

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.

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