Tools designed to extract main content from web pages require thorough evaluation, yet existing benchmarks disproportionately focus on English-language datasets. Consequently, previous studies have shown that while these extractors are well-optimized for English, their effectiveness partially or entirely diminishes in other languages. This study reproduces and extends recent benchmarks by incorporating multilingual datasets as a key factor. We analyze extractor performance across five languages-Greek, English, Polish, Russian, and Chinese-highlighting the need to adapt extraction models to linguistic variations. Our results show that while some extractors maintain stable performance, others suffer significant drops in precision and recall on non-English or structurally irregular pages.

Small and medium enterprises rely on detailed Web analytics to be informed about their market and competition. Focused crawlers meet this demand by crawling and indexing specific parts of the Web. Critically, a focused crawler must quickly find new pages that have not yet been indexed. Since a new page can be discovered only by following a new outlink, predicting new outlinks is very relevant in practice. In the literature, many feature designs have been proposed for predicting changes in the Web. In this work we provide a structured analysis of this problem, using new outlinks as our running prediction target. Specifically, we unify earlier feature designs in a taxonomic arrangement of features along two dimensions: static versus dynamic features, and features of a page versus features of the network around it. Within this taxonomy, complemented by our new (mainly, dynamic network) features, we identify best predictors for new outlinks. Our main conclusion is that most informative features are the recent history of new outlinks on a page itself, and of its content-related pages. Hence, we propose a new ‘look back, look around’ (LBLA) model, that uses only these features. With the obtained predictions, we design a number of scoring functions to guide a focused crawler to pages with most new outlinks, and compare their performance. The LBLA approach proved extremely effective, outperforming other models including those that use a most complete set of features. One of the learners we use, is the recent NGBoost method that assumes a Poisson distribution for the number of new outlinks on a page, and learns its parameters. This connects the two so far unrelated avenues in the literature: predictions based on features of a page, and those based on probabilistic modeling. All experiments were carried out on an original dataset, made available by a commercial focused crawler.