  We propose Visual News Captioner, an entity-aware model for the task of news
image captioning. We also introduce Visual News, a large-scale benchmark
consisting of more than one million news images along with associated news
articles, image captions, author information, and other metadata. Unlike the
standard image captioning task, news images depict situations where people,
locations, and events are of paramount importance. Our proposed method can
effectively combine visual and textual features to generate captions with
richer information such as events and entities. More specifically, built upon
the Transformer architecture, our model is further equipped with novel
multi-modal feature fusion techniques and attention mechanisms, which are
designed to generate named entities more accurately. Our method utilizes much
fewer parameters while achieving slightly better prediction results than
competing methods. Our larger and more diverse Visual News dataset further
highlights the remaining challenges in captioning news images.
