Generating Summaries with Topic Templates and Structured Convolutional Decoders

Laura Perez-Beltrachini, Yang Liu, Mirella Lapata

Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential decoders on three data sets representing different domains. Automatic and human evaluation demonstrate that our summaries have better content coverage.

Knowledge Graph



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