Key points are not available for this paper at this time.
Web Page segmentation is a crucial step for many applications in Information Retrieval, such as text classification, de-duplication and full-text search. In this paper we describe a new approach to segment HTML pages, building on methods from Quantitative Linguistics and strategies borrowed from the area of Computer Vision. We utilize the notion of text-density as a measure to identify the individual text segments of a web page, reducing the problem to solving a 1D-partitioning task. The distribution of segment-level text density seems to follow a negative hypergeometric distribution, described by Frumkina's Law. Our extensive evaluation confirms the validity and quality of our approach and its applicability to the Web.
Kohlschütter et al. (Sun,) studied this question.