  Extracting main content from web pages provides primary informative blocks
that remove a web page's minor areas like navigation menu, ads, and site
templates. The main content extraction has various applications: information
retrieval, search engine optimization, and browser reader mode. We assessed the
existing four main content extraction methods (Readability.js, Chrome DOM
Distiller, Web2Text, and Boilernet) with the web pages of two English datasets
from global websites of 2017 and 2020 and seven non-English datasets by
languages of 2020. Its result showed that performance was lower by up to 40% in
non-English datasets than in English datasets. Thus, this paper proposes a
multilingual main content extraction method using visual features: the
elements' positions, size, and distances from three centers. These centers were
derived from the browser window, web document, and the first browsing area. We
propose this first browsing area, which is the top side of a web document for
simulating situations where a user first encountered a web page. Because web
page authors placed their main contents in the central area for the web page's
usability, we can assume the center of this area is close to the main content.
Our grid-centering-expanding (GCE) method suggests the three centroids as
hypothetical user foci. Traversing the DOM tree from each of the leaf nodes
closest to these centroids, our method inspects which the ancestor node can be
the main content candidate. Finally, it extracts the main content by selecting
the best among the three main content candidates. Our method performed 14%
better than the existing method on average in Longest Common Subsequence F1
score. In particular, it improved performance by up to 25% in the English
dataset and 16% in the non-English dataset. Therefore, our method showed the
visual and basic HTML features are effective in extracting the main content.
