Multimodal Web Page Segmentation Using Self-organized Multi-objective Clustering

Abstract

Web page segmentation (WPS) aims to break a web page into different segments with coherent intra- and inter-semantics. By evidencing the morpho-dispositional semantics of a web page, WPS has traditionally been used to demarcate informative from non-informative content, but it has also evidenced its key role within the context of non-linear access to web information for visually impaired people. For that purpose, a great deal of ad hoc solutions have been proposed that rely on visual, logical, and/or text cues. However, such methodologies highly depend on manually tuned heuristics and are parameter-dependent. To overcome these drawbacks, principled frameworks have been proposed that provide the theoretical bases to achieve optimal solutions. However, existing methodologies only combine few discriminant features and do not define strategies to automatically select the optimal number of segments. In this article, we present a multi-objective clustering technique called MCS that relies on \( K \) -means, in which (1) visual, logical, and text cues are all combined in a early fusion manner and (2) an evolutionary process automatically discovers the optimal number of clusters (segments) as well as the correct positioning of seeds. As such, our proposal is parameter-free, combines many different modalities, does not depend on manually tuned heuristics, and can be run on any web page without any constraint. An exhaustive evaluation over two different tasks, where (1) the number of segments must be discovered or (2) the number of clusters is fixed with respect to the task at hand, shows that MCS drastically improves over most competitive and up-to-date algorithms for a wide variety of external and internal validation indices. In particular, results clearly evidence the impact of the visual and logical modalities towards segmentation performance.

A DOM-structural Cohesion Analysis Approach for Segmentation of Modern Web Pages

“…In addition, the HEPS method [20], mentioned in the Webis-WebSeg-20 dataset [16] for comparison, utilizes text nodes and images to identify potential headings, corresponding blocks, and create a hierarchical segmentation. The DOM structure is also a vital component in other segmentation models [14,15], where additional factors like textual and visual cues are integrated to enhance performance.…”

“…Over time, many solutions have been proposed to address the segmentation problem using different approaches and learning strategies. The most commonly used techniques fall into several categories: ad-hoc approaches [7,29,6,18,25] (which rely on manually-tuned heuristics and parameter-dependent methods), theoretically-founded approaches [9,1] (based on graph-theoretic and classical clustering algorithms), computer vision approaches [13,11], and others (as mentioned in [14]). In general, these approaches share three key elements: visual, textual, and structural cues found on web pages.…”

Web Page Content Block Identification with Extended Block Properties

“…In comparison to Andrew Judith et al solution [37], our method defines as many content blocks as there are on the page, not limiting the number of blocks. In comparison to other segment number not fixed solutions [38], this method is faster, as it does not require two stages (to identify the number of clusters and then to divide the web page into this number of blocks) and extracts all possible content blocks from the web page. The blocks are not limited to text containing structured blocks only [39] and extract all, not only structured blocks [40].…”