  Multi-sentence summarization is a well studied problem in NLP, while
generating image descriptions for a single image is a well studied problem in
Computer Vision. However, for applications such as image cluster labeling or
web page summarization, summarizing a set of images is also a useful and
challenging task. This paper proposes the new task of multi-image
summarization, which aims to generate a concise and descriptive textual summary
given a coherent set of input images. We propose a model that extends the
image-captioning Transformer-based architecture for single image to
multi-image. A dense average image feature aggregation network allows the model
to focus on a coherent subset of attributes across the input images. We explore
various input representations to the Transformer network and empirically show
that aggregated image features are superior to individual image embeddings. We
additionally show that the performance of the model is further improved by
pretraining the model parameters on a single-image captioning task, which
appears to be particularly effective in eliminating hallucinations in the
output.
