  Boilerplate removal refers to the problem of removing noisy content from a
webpage such as ads and extracting relevant content that can be used by various
services. This can be useful in several features in web browsers such as ad
blocking, accessibility tools such as read out loud, translation, summarization
etc. In order to create a training dataset to train a model for boilerplate
detection and removal, labeling or tagging webpage data manually can be tedious
and time consuming. Hence, a semi-supervised model, in which some of the
webpage elements are labeled manually and labels for others are inferred based
on some parameters, can be useful. In this paper we present a solution for
extraction of relevant content from a webpage that relies on semi-supervised
learning using Gaussian Random Fields. We first represent the webpage as a
graph, with text elements as nodes and the edge weights representing similarity
between nodes. After this, we label a few nodes in the graph using heuristics
and label the remaining nodes by a weighted measure of similarity to the
already labeled nodes. We describe the system architecture and a few
preliminary results on a dataset of webpages.
