  As the number of documents on the web is growing exponentially,
multi-document summarization is becoming more and more important since it can
provide the main ideas in a document set in short time. In this paper, we
present an unsupervised centroid-based document-level reconstruction framework
using distributed bag of words model. Specifically, our approach selects
summary sentences in order to minimize the reconstruction error between the
summary and the documents. We apply sentence selection and beam search, to
further improve the performance of our model. Experimental results on two
different datasets show significant performance gains compared with the
state-of-the-art baselines.
