  A common method for extractive multi-document news summarization is to
re-formulate it as a single-document summarization problem by concatenating all
documents as a single meta-document. However, this method neglects the relative
importance of documents. We propose a simple approach to reorder the documents
according to their relative importance before concatenating and summarizing
them. The reordering makes the salient content easier to learn by the
summarization model. Experiments show that our approach outperforms previous
state-of-the-art methods with more complex architectures.
