论文标题
图表到文本：通过调整变压器模型来生成图表的自然语言描述
Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
论文作者
论文摘要
诸如条形图和线路图之类的信息可视化非常流行，可探索数据和交流见解。解释和理解这种可视化可能对某些人来说是具有挑战性的，例如那些视力障碍或可视化素养较低的人。在这项工作中，我们介绍了一个新的数据集，并提出了一种神经模型，以自动为图表生成自然语言摘要。生成的摘要提供了图表的解释，并传达了该图表中发现的关键见解。我们的神经模型是通过扩展数据到文本生成任务的最新模型来开发的，该模型利用基于变压器的编码器架构体系结构。我们发现，我们的方法在内容选择指标上的基本模型的范围宽了（55.42％比8.49％），并产生更有信息，简洁和相干的摘要。
Information visualizations such as bar charts and line charts are very popular for exploring data and communicating insights. Interpreting and making sense of such visualizations can be challenging for some people, such as those who are visually impaired or have low visualization literacy. In this work, we introduce a new dataset and present a neural model for automatically generating natural language summaries for charts. The generated summaries provide an interpretation of the chart and convey the key insights found within that chart. Our neural model is developed by extending the state-of-the-art model for the data-to-text generation task, which utilizes a transformer-based encoder-decoder architecture. We found that our approach outperforms the base model on a content selection metric by a wide margin (55.42% vs. 8.49%) and generates more informative, concise, and coherent summaries.
