Multi-Document Summarization with Centroid-Based Pretraining
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In multi-document summarization (MDS), the input is a cluster of documents, and the output is the cluster summary. In this paper, we focus on pretraining objectives for MDS. Specifically, we introduce a simple pretraining objective of choosing 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 used for pretraining on a dataset containing only clusters of documents. Through zero-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 release our pretrained and finetuned models at https://github.com/ratishsp/centrum.
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