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Content Selection in Deep Learning Models of Summarization
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of state of the art extractive summarizers do not improve performance over simpler models. These results suggest that it is easier to create a summarizer for a new domain than previous work suggests and bring into question the benefit of deep learningdoi:10.18653/v1/d18-1208 dblp:conf/emnlp/KedzieMD18 fatcat:tfmjmj6ypnbv5bgvzhvbk5vcvi