Due to the success of the pre-trained language model (PLM), existing PLM-based summarization models show their powerful generative capability. However, these models are trained on general-purpose summarization datasets, leading to generated summaries failing to satisfy the needs of different readers. To generate summaries with topics, many efforts have been made on topic-focused summarization. However, these works generate a summary only guided by a prompt comprising topic words. Despite their success, these methods still ignore the disturbance of sentences with non-relevant topics and only conduct cross-interaction between tokens by attention module. To address this issue, we propose a topic-arc recognition objective and topic-selective graph network. First, the topic-arc recognition objective is used to model training, which endows the capability to discriminate topics for the model. Moreover, the topic-selective graph network can conduct topic-guided cross-interaction on sentences based on the results of topic-arc recognition. In the experiments, we conduct extensive evaluations on NEWTS and COVIDET datasets. Results show that our methods achieve state-of-the-art performance.

翻译：鉴于预训练语言模型（PLM）的成功，现有基于PLM的摘要模型展现出强大的生成能力。然而，这些模型在通用摘要数据集上训练，导致生成的摘要无法满足不同读者的需求。为生成包含主题的摘要，已有诸多研究致力于主题聚焦摘要。不过，这些方法仅通过包含主题词的提示来生成摘要。尽管取得了成功，这些方法仍忽略了非相关主题句子的干扰，并且仅通过注意力模块在词元间进行交叉交互。为解决此问题，我们提出了主题弧识别目标和主题选择图网络。首先，利用主题弧识别目标进行建模训练，赋予模型区分主题的能力。此外，主题选择图网络可基于主题弧识别结果，在句子间进行主题引导的交叉交互。在实验中，我们在NEWTS和COVIDET数据集上进行了广泛评估。结果表明，我们的方法达到了最先进的性能。