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.

提出了一种新颖的深度子模函数，该函数可以根据输入文档的地面真实摘要提取多模态内容，并明确确保文字和图像的覆盖范围、多样性和对齐性。在此基础上，使用基于LLM的复述器生成以输入内容为条件的具有各种设计方面的模板，并通过广泛的自动化和人工评估展示了我们方法的优点。

基于深度次模优化：从长篇多模态文档生成海报