In the biomedical domain, figures in the scientific articles attribute significantly in understanding the core concepts. However, these figures are always difficult to interpret by the humans as well as machines and, thus, associated texts in the article are required to summarize the figures. This article proposes an unsupervised automatic summarization system for individual figures present in a scientific biomedical article, where different quality measures capturing relevance of the sentences to the figure are simultaneously optimized using the search capability of a multiobjective optimization technique to obtain a good set of sentences in the summary. A newly designed self-organizing map based genetic operator helping in new solution generation is also introduced in the multiobjective optimization framework. For evaluation of the proposed technique, 94 and 81 figures over two datasets from the biomedical literature are used. Our proposed system, namely MOOFigSum, obtains 5% and 11% improvements in terms of F1-measure metric over the unsupervised technique for both datasets, respectively, while in comparison to supervised techniques, MOOFigSum obtains 9% and 2% improvements over these datasets, respectively.

中文翻译：

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图总结：一种基于多目标优化的方法

在生物医学领域，科学文章中的数字对于理解核心概念具有重要意义。然而，这些数字总是难以被人类和机器解释，因此需要文章中的相关文本来概括这些数字。本文提出了一种针对科学生物医学文章中单个图形的无监督自动摘要系统，其中使用多目标优化技术的搜索能力同时优化捕获句子与图形相关性的不同质量度量，以获得一组好的句子摘要。多目标优化框架中还引入了一种新设计的基于自组织图的遗传算子，有助于生成新的解决方案。为了评估所提出的技术，使用了来自生物医学文献的两个数据集上的 94 和 81 个数字。我们提出的系统，即 MOOFigSum，在 F1-measure 指标方面分别比两个数据集的无监督技术提高了 5% 和 11%，而与监督技术相比，MOOFigSum 在这些数据集上获得了 9% 和 2% 的改进，分别。