A Benchmark Suite for Template Detection and Content Extraction

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.