Neural Data-Driven Captioning of Time-Series Line Charts

Andrea Spreafico, G. Carenini
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引用次数: 16

Abstract

The success of neural methods for image captioning suggests that similar benefits can be reaped for generating captions for information visualizations. In this preliminary study, we focus on the very popular line charts. We propose a neural model which aims to generate text from the same data used to create a line chart. Due to the lack of suitable training corpora, we collected a dataset through crowdsourcing. Experiments indicate that our model outperforms relatively simple non-neural baselines.
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时间序列折线图的神经数据驱动字幕
神经方法用于图像字幕的成功表明,为信息可视化生成字幕也可以获得类似的好处。在这项初步研究中,我们将重点放在非常流行的折线图上。我们提出了一个神经模型,旨在从用于创建折线图的相同数据中生成文本。由于缺乏合适的训练语料库,我们通过众包的方式收集了一个数据集。实验表明,我们的模型优于相对简单的非神经基线。
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