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A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization

2017-07-21 · WS 2017 9 · Xinyu Hua, Lu Wang

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, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.

📄 PDF Abstract BibTeX arXiv:1707.07062

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Abstractive Text SummarizationArticlesDomain Adaptation

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