Effective figure captions are crucial for clear comprehension of scientific figures, yet poor caption writing remains a common issue in scientific articles. Our study of arXiv cs.CL papers found that 53.88% of captions were rated as unhelpful or worse by domain experts, showing the need for better caption generation. Previous efforts in figure caption generation treated it as a vision task, aimed at creating a model to understand visual content and complex contextual information. Our findings, however, demonstrate that over 75% of figure captions' tokens align with corresponding figure-mentioning paragraphs, indicating great potential for language technology to solve this task. In this paper, we present a novel approach for generating figure captions in scientific documents using text summarization techniques. Our approach extracts sentences referencing the target figure, then summarizes them into a concise caption. In the experiments on real-world arXiv papers (81.2% were published at academic conferences), our method, using only text data, outperformed previous approaches in both automatic and human evaluations. We further conducted data-driven investigations into the two core challenges: (i) low-quality author-written captions and (ii) the absence of a standard for good captions. We found that our models could generate improved captions for figures with original captions rated as unhelpful, and the model trained on captions with more than 30 tokens produced higher-quality captions. We also found that good captions often include the high-level takeaway of the figure. Our work proves the effectiveness of text summarization in generating figure captions for scholarly articles, outperforming prior vision-based approaches. Our findings have practical implications for future figure captioning systems, improving scientific communication clarity.

翻译：摘要：有效的图表标题对于清晰理解科学图表至关重要，然而在科学文章中，标题撰写不当仍是一个普遍问题。我们对arXiv计算机科学-计算语言学（cs.CL）论文的研究发现，领域专家将53.88%的标题评为“无帮助”或更差，这表明了改进标题生成的必要性。以往图表标题生成的研究将其视为视觉任务，旨在构建能理解视觉内容及复杂上下文信息的模型。然而，我们的发现表明，超过75%的图表标题中的标记与提及该图表的对应段落相符，这表明语言技术解决此任务具有巨大潜力。本文提出了一种新颖方法，利用文本摘要技术为科学文档生成图表标题。该方法提取引用目标图表的句子，并将其总结为简洁标题。在针对真实arXiv论文（其中81.2%已在学术会议上发表）的实验中，我们的方法仅使用文本数据，在自动评估和人工评估中均优于以往方法。我们进一步通过数据驱动研究探讨了两个核心挑战：（i）作者撰写的低质量标题，以及（ii）缺乏优秀标题的标准。我们发现，对于原始标题被评为“无帮助”的图表，我们的模型能够生成改进后的标题；而基于超过30个标记的标题训练的模型生成了更高质量的标题。此外，优秀标题往往包含图表的高层次要点。本研究证明了文本摘要技术在生成学术文章图表标题中的有效性，超越了以往基于视觉的方法。我们的发现对未来图表标题系统的开发具有实际意义，有助于提升科学交流的清晰度。