  Understanding the nature of high-quality summaries is crucial to further
improve the performance of multi-document summarization. We propose an approach
to characterize human-written summaries using partial information
decomposition, which decomposes the mutual information provided by all source
documents into union, redundancy, synergy, and unique information. Our
empirical analysis on different MDS datasets shows that there is a direct
dependency between the number of sources and their contribution to the summary.
