  Advancements in Text-to-Image synthesis over recent years have focused more
on improving the quality of generated samples using datasets with descriptive
prompts. However, real-world image-caption pairs present in domains such as
news data do not use simple and directly descriptive captions. With captions
containing information on both the image content and underlying contextual
cues, they become abstractive in nature. In this paper, we launch ANNA, an
Abstractive News captioNs dAtaset extracted from online news articles in a
variety of different contexts. We explore the capabilities of current
Text-to-Image synthesis models to generate news domain-specific images using
abstractive captions by benchmarking them on ANNA, in both standard training
and transfer learning settings. The generated images are judged on the basis of
contextual relevance, visual quality, and perceptual similarity to ground-truth
image-caption pairs. Through our experiments, we show that techniques such as
transfer learning achieve limited success in understanding abstractive captions
but still fail to consistently learn the relationships between content and
context features. The Dataset is available at
https://github.com/aashish2000/ANNA .
