SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning
This addresses the need for scalable and resource-efficient web information extraction for users dealing with large-scale semi-structured data, representing a novel method for a known bottleneck.
The paper tackled the problem of extracting structured information from semi-structured web content like HTML tables and lists, which is challenging due to formatting issues and resource-intensive methods, and introduced SCRIBES, a reinforcement learning framework that generates reusable extraction scripts, resulting in over 13% improvement in script quality and more than 4% boost in downstream question answering accuracy for GPT-4o.
Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complicates usage, and reliably extracting structured information from them remains challenging. Existing methods either lack generalization or are resource-intensive due to per-page LLM inference. In this paper, we introduce SCRIBES (SCRIpt-Based Semi-Structured Content Extraction at Web-Scale), a novel reinforcement learning framework that leverages layout similarity across webpages within the same site as a reward signal. Instead of processing each page individually, SCRIBES generates reusable extraction scripts that can be applied to groups of structurally similar webpages. Our approach further improves by iteratively training on synthetic annotations from in-the-wild CommonCrawl data. Experiments show that our approach outperforms strong baselines by over 13% in script quality and boosts downstream question answering accuracy by more than 4% for GPT-4o, enabling scalable and resource-efficient web information extraction.