News image captioning requires model to generate an informative caption rich in entities, with the news image and the associated news article. Though Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in addressing various vision-language tasks, our research finds that current MLLMs still bear limitations in handling entity information on news image captioning task. Besides, while MLLMs have the ability to process long inputs, generating high-quality news image captions still requires a trade-off between sufficiency and conciseness of textual input information. To explore the potential of MLLMs and address problems we discovered, we propose : an Entity-Aware Multimodal Alignment based approach for news image captioning. Our approach first aligns the MLLM through Balance Training Strategy with two extra alignment tasks: Entity-Aware Sentence Selection task and Entity Selection task, together with News Image Captioning task, to enhance its capability in handling multimodal entity information. The aligned MLLM will utilizes the additional entity-related information it explicitly extracts to supplement its textual input while generating news image captions. Our approach achieves better results than all previous models in CIDEr score on GoodNews dataset (72.33 -> 88.39) and NYTimes800k dataset (70.83 -> 85.61).

翻译：新闻图像描述要求模型结合新闻图像与相关新闻报道，生成富含实体信息的描述性标题。尽管多模态大语言模型（MLLMs）在解决各类视觉-语言任务中展现出卓越能力，但本研究发现当前MLLMs在处理新闻图像描述任务的实体信息方面仍存在局限性。此外，尽管MLLMs具备处理长文本输入的能力，生成高质量的新闻图像标题仍需在文本输入信息的充分性与简洁性之间寻求平衡。为探索MLLMs的潜力并解决发现的问题，本文提出一种基于实体感知多模态对齐的新闻图像描述方法。该方法首先通过平衡训练策略，将MLLM与实体感知句子选择任务、实体选择任务这两项额外对齐任务及新闻图像描述任务协同训练，以增强其处理多模态实体信息的能力。经过对齐的MLLM将利用其显式提取的额外实体相关信息，在生成新闻图像标题时补充文本输入。本方法在GoodNews数据集（CIDEr得分从72.33提升至88.39）和NYTimes800k数据集（CIDEr得分从70.83提升至85.61）上均取得优于所有先前模型的结果。