  The most important obstacles facing multi-document summarization include
excessive redundancy in source descriptions and the looming shortage of
training data. These obstacles prevent encoder-decoder models from being used
directly, but optimization-based methods such as determinantal point processes
(DPPs) are known to handle them well. In this paper we seek to strengthen a
DPP-based method for extractive multi-document summarization by presenting a
novel similarity measure inspired by capsule networks. The approach measures
redundancy between a pair of sentences based on surface form and semantic
information. We show that our DPP system with improved similarity measure
performs competitively, outperforming strong summarization baselines on
benchmark datasets. Our findings are particularly meaningful for summarizing
documents created by multiple authors containing redundant yet lexically
diverse expressions.
