  In Multi-Document Summarization (MDS), the input can be modeled as a set of
documents, and the output is its summary. In this paper, we focus on
pretraining objectives for MDS. Specifically, we introduce a novel pretraining
objective, which involves selecting the ROUGE-based centroid of each document
cluster as a proxy for its summary. Our objective thus does not require human
written summaries and can be utilized for pretraining on a dataset consisting
solely of document sets. Through zero-shot, few-shot, and fully supervised
experiments on multiple MDS datasets, we show that our model Centrum is better
or comparable to a state-of-the-art model. We make the pretrained and
fine-tuned models freely available to the research community
https://github.com/ratishsp/centrum.
