Automatic text summarization has experienced substantial progress in recent years. With this progress, the question has arisen whether the types of summaries that are typically generated by automatic summarization models align with users' needs. Ter Hoeve et al (2020) answer this question negatively. Amongst others, they recommend focusing on generating summaries with more graphical elements. This is in line with what we know from the psycholinguistics literature about how humans process text. Motivated from these two angles, we propose a new task: summarization with graphical elements, and we verify that these summaries are helpful for a critical mass of people. We collect a high quality human labeled dataset to support research into the task. We present a number of baseline methods that show that the task is interesting and challenging. Hence, with this work we hope to inspire a new line of research within the automatic summarization community.
翻译:近年来,自动文本汇总取得了实质性进展。 随着这一进展, 出现了自动汇总模型通常产生的摘要类型是否符合用户需要的问题。 Ter Hoeve 等人( 202020年) 否定了这个问题。 除其他外, 他们建议侧重于以更多图形元素生成摘要。 这符合我们从心理语言学文献中了解到的关于人类过程文本的知识。 我们从这两个角度提出了一个新的任务: 图形元素汇总, 我们核实这些摘要对足够数量的人有用。 我们收集了一个高质量的人类标签数据集来支持对任务的研究。 我们提出了一些基线方法, 表明任务有趣而富有挑战性。 因此, 我们希望通过这项工作, 在自动汇总社区内激发新的研究线。