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Current deep learning models often achieve excellent results on benchmarkimage-to-text datasets but fail to generate texts that are useful in practice.We argue that to close this gap, it is vital to distinguish descriptions fromcaptions based on their distinct communicative roles. Descriptions focus onvisual features and are meant to replace an image (often to increaseaccessibility), whereas captions appear alongside an image to supply additionalinformation. To motivate this distinction and help people put it into practice,we introduce the publicly available Wikipedia-based dataset Concadia consistingof 96,918 images with corresponding English-language descriptions, captions,and surrounding context. Using insights from Concadia, models trained on it,and a preregistered human-subjects experiment with human- and model-generatedtexts, we characterize the commonalities and differences between descriptionsand captions. In addition, we show that, for generating both descriptions andcaptions, it is useful to augment image-to-text models with representations ofthe textual context in which the image appeared.

Concadia: Towards Image-Based Text Generation with a Purpose