Co-Scraper: query-aware DOM Pruning and Reusable Scraper Synthesis for Lightweight Web Data Extraction
arXiv:2606.14821v1 Announce Type: cross Abstract: The abundant and heterogeneous nature of web content necessitates automated information extraction, and generating scrapers that can be reused across similar web pages offers an effective solution for scalable data extraction. In this work, we propose Co-Scraper, a two-stage framework capable of handling the hierarchical complexity of long HTML documents. By integrating a query-aware DOM pruning mechanism with stable extraction strategy induction, Co-Scraper can effectively transforms web content into executable programmatic wrappers using a fi
The increasing complexity and volume of web content, coupled with the rising demand for automated data pipelines, necessitates more sophisticated and efficient web scraping solutions.
This development represents a significant step towards more autonomous and robust data extraction from the web, crucial for AI training, competitive intelligence, and market analysis across various sectors.
The ability to generate reusable and query-aware scrapers will streamline data acquisition, reducing manual effort and increasing the scalability and reliability of web data pipelines.
- · AI data providers
- · Market intelligence firms
- · Competitive intelligence platforms
- · E-commerce aggregators
- · Manual data entry services
- · Companies with static data acquisition methods
Automated web data extraction becomes significantly more efficient and scalable.
An abundance of cleaner, more structured web data fuels advanced AI model training and analytics.
New forms of market analysis and business models emerge based on real-time, comprehensive web intelligence.
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Read at arXiv cs.AI