《原始论文：Text Summarization with Pretrained Encoders》
It is worth noting that our decoder applies neither a copy nor a coverage mechanism (See et al., 2017), despite their popularity in abstractive summarization. This is mainly because we focus on building a minimum-requirements model and these mechanisms may introduce additional hyper-parameters to tune. Thanks to the subwords tokenizer, we also rarely observe issues with out-of-vocabulary words in the output; moreover, trigram-blocking produces diverse summaries managing to reduce repetitions.
