Neural abstractive summarization models are flexible and can produce coherent
summaries, but they are sometimes unfaithful and can be difficult to control.
While previous studies attempt to provide different types of guidance to
control the output and increase faithfulness, it is not clear how these
strategies compare and contrast to each other. In this paper, we propose a
general and extensible guided summarization framework (GSum) that can
effectively take different kinds of external guidance as input, and we perform
experiments across several different varieties. Experiments demonstrate that
this model is effective, achieving state-of-the-art performance according to
ROUGE on 4 popular summarization datasets when using highlighted sentences as
guidance. In addition, we show that our guided model can generate more faithful
summaries and demonstrate how different types of guidance generate
qualitatively different summaries, lending a degree of controllability to the
learned models.

本文提出了一个通用可扩展的引导式摘要框架（GSum），可以有效地将不同种类的外部引导作为输入，通过实验表明了该模型在使用突出显示的句子作为引导时，在 4 个流行的摘要数据集上实现了 ROUGE 的最优性能，同时我们还展示了我们的引导模型可以生成更忠实的摘要，并展示了不同类型的引导所生成的不同摘要，为学习模型提供了一定程度的可控性。