  Template detection and content extraction are two of the main areas of
information retrieval applied to the Web. They perform different analyses over
the structure and content of webpages to extract some part of the document.
However, their objective is different. While template detection identifies the
template of a webpage (usually comparing with other webpages of the same
website), content extraction identifies the main content of the webpage
discarding the other part. Therefore, they are somehow complementary, because
the main content is not part of the template. It has been measured that
templates represent between 40% and 50% of data on the Web. Therefore,
identifying templates is essential for indexing tasks because templates usually
contain irrelevant information such as advertisements, menus and banners.
Processing and storing this information is likely to lead to a waste of
resources (storage space, bandwidth, etc.). Similarly, identifying the main
content is essential for many information retrieval tasks. In this paper, we
present a benchmark suite to test different approaches for template detection
and content extraction. The suite is public, and it contains real heterogeneous
webpages that have been labelled so that different techniques can be suitable
(and automatically) compared.
