  Till now, neural abstractive summarization methods have achieved great
success for single document summarization (SDS). However, due to the lack of
large scale multi-document summaries, such methods can be hardly applied to
multi-document summarization (MDS). In this paper, we investigate neural
abstractive methods for MDS by adapting a state-of-the-art neural abstractive
summarization model for SDS. We propose an approach to extend the neural
abstractive model trained on large scale SDS data to the MDS task. Our approach
only makes use of a small number of multi-document summaries for fine tuning.
Experimental results on two benchmark DUC datasets demonstrate that our
approach can outperform a variety of baseline neural models.
