  A poster from a long input document can be considered as a one-page
easy-to-read multimodal (text and images) summary presented on a nice template
with good design elements. Automatic transformation of a long document into a
poster is a very less studied but challenging task. It involves content
summarization of the input document followed by template generation and
harmonization. In this work, we propose a novel deep submodular function which
can be trained on ground truth summaries to extract multimodal content from the
document and explicitly ensures good coverage, diversity and alignment of text
and images. Then, we use an LLM based paraphraser and propose to generate a
template with various design aspects conditioned on the input content. We show
the merits of our approach through extensive automated and human evaluations.
