  While neural sequence learning methods have made significant progress in
single-document summarization (SDS), they produce unsatisfactory results on
multi-document summarization (MDS). We observe two major challenges when
adapting SDS advances to MDS: (1) MDS involves larger search space and yet more
limited training data, setting obstacles for neural methods to learn adequate
representations; (2) MDS needs to resolve higher information redundancy among
the source documents, which SDS methods are less effective to handle. To close
the gap, we present RL-MMR, Maximal Margin Relevance-guided Reinforcement
Learning for MDS, which unifies advanced neural SDS methods and statistical
measures used in classical MDS. RL-MMR casts MMR guidance on fewer promising
candidates, which restrains the search space and thus leads to better
representation learning. Additionally, the explicit redundancy measure in MMR
helps the neural representation of the summary to better capture redundancy.
Extensive experiments demonstrate that RL-MMR achieves state-of-the-art
performance on benchmark MDS datasets. In particular, we show the benefits of
incorporating MMR into end-to-end learning when adapting SDS to MDS in terms of
both learning effectiveness and efficiency.
