In this paper, we address the task of news-image captioning, which generates a description of an image given the image and its article body as input. This task is more challenging than the conventional image captioning, because it requires a joint understanding of image and text. We present a Transformer model that integrates text and image modalities and attends to textual features from visual features in generating a caption. Experiments based on automatic evaluation metrics and human evaluation show that an article text provides primary information to reproduce news-image captions written by journalists. The results also demonstrate that the proposed model outperforms the state-of-the-art model. In addition, we also confirm that visual features contribute to improving the quality of news-image captions.
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