Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization

Understand

Content-Controllable Summarization generates summaries focused on the given controlling signals.

• Due to the lack of large-scale training corpora for the task, we propose a plug-and-play module RelAttn to adapt any general summarizers to the content-controllable summarization task.
• RelAttn first identifies the relevant content in the source documents, and then makes the model attend to the right context by directly steering the attention weight.
• We further apply an unsupervised online adaptive parameter searching algorithm to determine the degree of control in the zero-shot setting, while such parameters are learned in the few-shot setting.