The task of news article image captioning aims to generate descriptive and
informative captions for news article images. Unlike conventional image
captions that simply describe the content of the image in general terms, news
image captions follow journalistic guidelines and rely heavily on named
entities to describe the image content, often drawing context from the whole
article they are associated with. In this work, we propose a new approach to
this task, motivated by caption guidelines that journalists follow. Our
approach, Journalistic Guidelines Aware News Image Captioning (JoGANIC),
leverages the structure of captions to improve the generation quality and guide
our representation design. Experimental results, including detailed ablation
studies, on two large-scale publicly available datasets show that JoGANIC
substantially outperforms state-of-the-art methods both on caption generation
and named entity related metrics.

本篇论文提出了一种新的新闻文章图片标题生成方法，名为 JoGANIC，该方法基于新闻工作者遵循的标题指南，利用标题结构来提高生成质量和引导表示设计，实验结果表明，该方法在生成质量和命名实体相关指标上优于现有方法。