We propose a unified model combining the strength of extractive and
abstractive summarization. On the one hand, a simple extractive model can
obtain sentence-level attention with high ROUGE scores but less readable. On
the other hand, a more complicated abstractive model can obtain word-level
dynamic attention to generate a more readable paragraph. In our model,
sentence-level attention is used to modulate the word-level attention such that
words in less attended sentences are less likely to be generated. Moreover, a
novel inconsistency loss function is introduced to penalize the inconsistency
between two levels of attentions. By end-to-end training our model with the
inconsistency loss and original losses of extractive and abstractive models, we
achieve state-of-the-art ROUGE scores while being the most informative and
readable summarization on the CNN/Daily Mail dataset in a solid human
evaluation.

研究人员提出了一种统一模型，结合抽取性与生成性摘要的优势，通过句子级别的关注来调节单词级别的关注，采用新型的不一致损失函数，通过端到端的训练，实现了在 CNN / 每日邮报数据集上最先进的 ROUGE 分数和最信息化和可读性的摘要。