论文标题
参加正确的上下文：用于内容控制的摘要的插件模块
Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization
论文作者
论文摘要
Content Controllable摘要生成的摘要集中在给定的控制信号上。由于缺乏大规模的培训语料库，我们提出了一个插件模块，以使任何一般摘要都适应可控制的摘要任务。 Relattn首先识别源文档中的相关内容，然后通过直接转向注意力重量来使模型进入正确的上下文。我们进一步应用了无监督的在线自适应参数搜索算法，以确定零拍设置中的控制程度，而在几个弹片设置中学习了此类参数。通过将模块应用于三个主干摘要模型，实验表明，我们的方法有效地改善了所有摘要器，并且在零和几次设置中都超过了基于前缀的方法和广泛使用的插入和播放模型。可以说，在需要更多控制的情况下，在情况下观察到更多的好处。
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. By applying the module to three backbone summarization models, experiments show that our method effectively improves all the summarizers, and outperforms the prefix-based method and a widely used plug-and-play model in both zero- and few-shot settings. Tellingly, more benefit is observed in the scenarios when more control is needed.
