The segmentation of HTML documents has been essential to information extraction tasks, as showed by several works in this area. This paper studies the link between an HTML document and its visual representation to show how it helps segments identification using a structural approach. For this, we investigate how tree edit distance algorithms can find structural similarities in a DOM tree, using two tasks to execute our experiments. The first one is the identification of genuine tables where we obtained a 90.40% F1 score using the corpus provided by (Wang e Hu, 2002). We show through an experimental study that this result is competitive with the best results in the area. The second task studied is the identification of product listings in e-commerce sites. Here we get a 94.95% F1 score using a corpus with 1114 HTML documents from 8 distinct sites. We conclude that algorithms to calculate trees similarity provide competitive results for both tasks, making them also good candidates to identify other types of segments.