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We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, thedoi:10.18653/v1/w17-4513 dblp:conf/emnlp/HuaW17 fatcat:53kmpbiz5favjb45emdavasfmy