Browsing on Small Screens: Recasting Web-Page Segmentation into an Efficient Machine Learning Framework

摘要

Fitting enough information from webpages to make browsing on small screens compelling is a challenging task. One approach is to present the user with a thumbnail image of the full web page and allow the user to simply press a single key to zoom into a region (which may then be transcoded into wml/xhtml, summarized, etc). However, if regions for zooming are presented naively, this yields a frustrating experience because of the number of coherent regions, sentences, images, and words that may be inadvertently separated. Here, we cast the web page segmentation problem into a machine learning framework, where we re-examine this task through the lens of entropy reduction and decision tree learning. This yields an efficient and effective page segmentation algorithm. We demonstrate how simple techniques from computer vision can be used to fine-tune the results. The resulting segmentation keeps coherent regions together when tested on a broad set of complex webpages.