The problem of poster generation for scientific papers is under-investigated. Posters often present the most important information of papers, and the task can be considered as a special form of document summarization. Previous studies focus mainly on poster layout and panel composition, while neglecting the importance of content extraction. Besides, their datasets are not publicly available, which hinders further research. In this paper, we construct a benchmark dataset from scratch for this task. Then we propose a three-step framework to tackle this task and focus on the content extraction step in this study. To get both textual and visual elements of a poster panel, a neural extractive model is proposed to extract text, figures and tables of a paper section simultaneously. We conduct experiments on the dataset and also perform ablation study. Results demonstrate the efficacy of our proposed model. The dataset and code will be released.
翻译:科学论文的海报制作问题调查不足,海报往往提供最重要的文件信息,任务可被视为一种特殊的文件总结形式。以前的研究主要侧重于海报布局和小组组成,而忽视了内容提取的重要性。此外,它们的数据集不能公开提供,这妨碍了进一步的研究。在本文件中,我们从零开始为这项任务建立一个基准数据集。然后,我们提出一个处理这项任务的三步框架,并侧重于本研究的内容提取步骤。为了获得海报面板的文字和视觉要素,我们提议了一个神经采掘模型,以同时提取纸张部分的文本、图表和表格。我们在数据集上进行实验,并进行减缩研究。结果显示了我们提议的模型的有效性。数据集和代码将予公布。