PLAtE: A Large-scale Dataset for List Page Web Extraction

TL;DR

PLAtE is a large-scale dataset designed for web extraction tasks focusing on shopping review pages, enabling improved training of neural models for list page information extraction.

Contribution

The paper introduces PLAtE, the first large-scale dataset for list page web extraction, and evaluates state-of-the-art models on this new challenging benchmark.

Findings

Models show varying strengths and weaknesses on the tasks.

PLAtE enables better training for neural web extraction models.

The dataset covers nearly 53,000 items from over 6,600 pages.

Abstract

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.