News captioning task aims to generate sentences by describing named entities or concrete events for an image with its news article. Existing methods have achieved remarkable results by relying on the large-scale pre-trained models, which primarily focus on the correlations between the input news content and the output predictions. However, the news captioning requires adhering to some fundamental rules of news reporting, such as accurately describing the individuals and actions associated with the event. In this paper, we propose the rule-driven news captioning method, which can generate image descriptions following designated rule signal. Specifically, we first design the news-aware semantic rule for the descriptions. This rule incorporates the primary action depicted in the image (e.g., "performing") and the roles played by named entities involved in the action (e.g., "Agent" and "Place"). Second, we inject this semantic rule into the large-scale pre-trained model, BART, with the prefix-tuning strategy, where multiple encoder layers are embedded with news-aware semantic rule. Finally, we can effectively guide BART to generate news sentences that comply with the designated rule. Extensive experiments on two widely used datasets (i.e., GoodNews and NYTimes800k) demonstrate the effectiveness of our method.

翻译：新闻图像描述生成任务旨在为配有其新闻文章的图像生成描述命名实体或具体事件的句子。现有方法通过依赖大规模预训练模型取得了显著成果，这些模型主要关注输入新闻内容与输出预测之间的关联。然而，新闻图像描述生成需要遵循新闻报道的基本规则，例如准确描述与事件相关的个体及其行为。在本文中，我们提出了一种规则驱动的新闻图像描述生成方法，该方法能够根据指定的规则信号生成图像描述。具体而言，我们首先为描述设计了一种新闻感知语义规则。该规则包含了图像中描绘的主要动作（例如“表演”）及参与该动作的命名实体所扮演的角色（例如“施动者”和“地点”）。其次，我们通过前缀调优策略将此语义规则注入大规模预训练模型BART，其中多个编码器层嵌入了新闻感知语义规则。最后，我们能够有效引导BART生成符合指定规则的新闻句子。在两个广泛使用的数据集（即GoodNews和NYTimes800k）上进行的大量实验证明了我们方法的有效性。