Abstract Summary
Segmentation is a crucial prerequisite for effective and efficient information retrieval on websites, as it enables the structured interpretation of heterogeneous content. Recently, a novel dataset has been released that provides two complementary segmentation schemes: a broad functional segmentation and a niche segmentation based on website digital maturity. While the former captures general structural elements, the latter targets a more specialized classification task, creating an interesting challenge for state-of-the-art segmentation approaches. In this paper, we present the first comprehensive evaluation of visual and textual models on this dataset, ranging from basic rule-based methods to large language models. We assess their performance across both segmentation frameworks using multiple evaluation scores. Our results show that visual approaches, despite limited training data, are generally more successful at generalizing across website structures and consistently outperform textual models. Notably, ResNet18 achieves the strongest performance in both functional and maturity-based segmentation, which we attribute to its ability to effectively capture and integrate both global and local context of a webpage. These findings establish important baselines for future research and underscore the importance of developing models that can perform robustly in niche settings and under data-scarce conditions.
