Figure Summarization: A Multiobjective Optimization-Based Approach

Abstract

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.