We introduce a simple but flexible mechanism to learn an intermediate plan to
ground the generation of abstractive summaries. Specifically, we prepend (or
prompt) target summaries with entity chains -- ordered sequences of entities
mentioned in the summary. Transformer-based sequence-to-sequence models are
then trained to generate the entity chain and then continue generating the
summary conditioned on the entity chain and the input. We experimented with
both pretraining and finetuning with this content planning objective. When
evaluated on CNN/DailyMail, XSum, SAMSum and BillSum, we demonstrate
empirically that the grounded generation with the planning objective improves
entity specificity and planning in summaries for all datasets, and achieves
state-of-the-art performance on XSum and SAMSum in terms of Rouge. Moreover, we
demonstrate empirically that planning with entity chains provides a mechanism
to control hallucinations in abstractive summaries. By prompting the decoder
with a modified content plan that drops hallucinated entities, we outperform
state-of-the-art approaches for faithfulness when evaluated automatically and
by humans.

本文提出了利用实体链策略来帮助生成摘要的方法，通过在目标摘要前附加实体链从而生成更为准确与可控制的摘要，该方法在多个数据集上得到应用，并在 faithfulness 方面表现优异。