The growing interest in developing corpora of persuasive texts has promoted
applications in automated systems, e.g., debating and essay scoring systems;
however, there is little prior work mining image persuasiveness from an
argumentative perspective. To expand persuasiveness mining into a multi-modal
realm, we present a multi-modal dataset, ImageArg, consisting of annotations of
image persuasiveness in tweets. The annotations are based on a persuasion
taxonomy we developed to explore image functionalities and the means of
persuasion. We benchmark image persuasiveness tasks on ImageArg using
widely-used multi-modal learning methods. The experimental results show that
our dataset offers a useful resource for this rich and challenging topic, and
there is ample room for modeling improvement.

本研究的重点在于将具有说服力的图像转换为多模式数据，开发用于自动化系统的多模式数据集，然后使用这个数据集对图像说服力任务进行基准测试和实验，通过开发一种说服分类方法来划分图像的说服功能和说服方式，为这一高级领域提供有用的资源。

ImageArg：用于图像说服挖掘的多模态推特数据集

ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining

This literature review focuses on the use of Natural Language Generation
(NLG) to automatically detect and generate persuasive texts. Extending previous
research on automatic identification of persuasion in text, we concentrate on
generative aspects through conceptualizing determinants of persuasion in five
business-focused categories: benevolence, linguistic appropriacy, logical
argumentation, trustworthiness, tools and datasets. These allow NLG to increase
an existing message's persuasiveness. Previous research illustrates key aspects
in each of the above mentioned five categories. A research agenda to further
study persuasive NLG is developed. The review includes analysis of
seventy-seven articles, outlining the existing body of knowledge and showing
the steady progress in this research field.

此文献综述聚焦于使用自然语言生成 (NLG) 自动检测和生成说服性文本的应用，旨在延伸之前研究的自动识别文本中的说服力的方面，通过将说服力的决定因素概念化为五个业务关注的类别：仁慈、语言适宜性、逻辑论证、可靠性、工具和数据集，重点关注生成方面，这些因素可以使 NLG 增加现有信息的说服力，并发展了进一步研究说服性 NLG 的研究议程，该综述分析了 77 篇文章，概述了现有的知识体系，展示了这一研究领域的稳步进展。