Repurposing Decoder-Transformer Language Models for Abstractive Summarization

1 Sep 2019Luke de OliveiraAlfredo Láinez Rodrigo

Neural network models have shown excellent fluency and performance when applied to abstractive summarization. Many approaches to neural abstractive summarization involve the introduction of significant inductive bias, exemplified through the use of components such as pointer-generator architectures, coverage, and partially extractive procedures, designed to mimic the process by which humans summarize documents... (read more)

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