Co-Scraper: query-aware DOM Pruning and Reusable Scraper Synthesis for Lightweight Web Data Extraction

2026-06-12 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

Co-Scraper is a novel two-stage framework developed for automated and scalable web data extraction, specifically designed to handle the hierarchical complexity of long HTML documents. It addresses the critical need for generating scrapers that can be effectively reused across similar web pages. The framework integrates a query-aware Document Object Model (DOM) pruning mechanism with stable extraction strategy induction, transforming web content into executable programmatic wrappers. This process is powered by a fine-tuned Qwen3-8B model. Evaluated on the SWDE test set, Co-Scraper achieved an F1 score of 94.78% and demonstrated a reuse success rate of 90.39%, significantly enhancing the accuracy and resilience of web data acquisition.

Key takeaway

For Machine Learning Engineers or Data Scientists tasked with scalable web data acquisition, Co-Scraper offers a robust solution to improve extraction accuracy and resilience. If you are developing systems that require reusable scrapers across similar web pages, consider integrating query-aware DOM pruning and stable extraction strategy induction. This approach, demonstrated by Co-Scraper's 94.78% F1 score, can significantly streamline your data pipeline and reduce maintenance overhead for dynamic web content.

Key insights

Query-aware DOM pruning and stable strategy induction enable reusable web scrapers for scalable data extraction.

Principles

• Reusable scrapers enhance scalable data extraction.
• Integrating query-aware pruning with stable strategy induction improves extraction accuracy and resilience.

Method

Co-Scraper employs a two-stage framework: query-aware DOM pruning and stable extraction strategy induction, utilizing a fine-tuned Qwen3-8B model to synthesize executable programmatic wrappers.

In practice

• Automated information extraction from heterogeneous web content.
• Synthesizing scrapers reusable across similar web pages.

Topics

• Web Data Extraction
• DOM Pruning
• Scraper Synthesis
• Qwen3-8B
• Information Retrieval
• HTML Parsing

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.