  Recently, neural models have been leveraged to significantly improve the
performance of information extraction from semi-structured websites. However, a
barrier for continued progress is the small number of datasets large enough to
train these models. In this work, we introduce the PLAtE (Pages of Lists
Attribute Extraction) benchmark dataset as a challenging new web extraction
task. PLAtE focuses on shopping data, specifically extractions from product
review pages with multiple items encompassing the tasks of: (1) finding
product-list segmentation boundaries and (2) extracting attributes for each
product. PLAtE is composed of 52, 898 items collected from 6, 694 pages and
156, 014 attributes, making it the first largescale list page web extraction
dataset. We use a multi-stage approach to collect and annotate the dataset and
adapt three state-of-the-art web extraction models to the two tasks comparing
their strengths and weaknesses both quantitatively and qualitatively.
