Information Graphic Summarization using a Collection of Multimodal Deep Neural Networks

Edward J. Kim, Connor Onweller, Kathleen F. McCoy
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引用次数: 6

Abstract

We present a multimodal deep learning framework that can generate summarization text supporting the main idea of an information graphic for presentation to a person who is blind or visually impaired. The framework utilizes the visual, textual, positional, and size characteristics extracted from the image to create the summary. Different and complimentary neural architectures are optimized for each task using crowdsourced training data. From our quantitative experiments and results, we explain the reasoning behind our framework and show the effectiveness of our models. Our qualitative results showcase text generated from our framework and show that Mechanical Turk participants favor them to other automatic and human generated summarizations. We describe the design and results of an experiment to evaluate the utility of our system for people who have visual impairments in the context of understanding Twitter Tweets containing line graphs.
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使用多模态深度神经网络集合的信息图形总结
我们提出了一个多模态深度学习框架,该框架可以生成支持信息图形的主要思想的摘要文本,以呈现给盲人或视障人士。该框架利用从图像中提取的视觉、文本、位置和大小特征来创建摘要。使用众包训练数据对每个任务进行了不同的和互补的神经架构优化。从我们的定量实验和结果中,我们解释了我们的框架背后的推理,并展示了我们模型的有效性。我们的定性结果展示了从我们的框架中生成的文本,并表明Mechanical Turk参与者更喜欢它们而不是其他自动和人工生成的摘要。我们描述了一个实验的设计和结果,以评估我们的系统在理解包含线形图的Twitter推文的背景下对有视觉障碍的人的效用。
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