  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.
