Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Recent studies on single-document summarization (SDS) benefit from advances in neural sequence learning, but they produce unsatisfactory results on multi-document summary (MDS). |
| Approach: | They propose a neural sequence learning method that unifies advanced neural SDS methods and statistical measures used in classical MDS. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmark MDS datasets. |
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