🤖 AI Summary
This work addresses the challenges of structured information extraction from web pages, where handcrafted rules are brittle and large language models incur prohibitive computational costs. The authors propose a novel paradigm that treats the HTML DOM as a prunable tree, employing a dedicated pruning mechanism to retain high-information-density subtrees. A lightweight 0.6B-parameter language model is then applied for zero-shot cross-domain extraction. To ensure result reliability, the approach incorporates a traceable Grounded XPath Resolution (GXR) mechanism. Evaluated on the SWDE dataset, the method achieves an F1 score of 88.1%, outperforming several larger, fully trained baseline models while substantially reducing computational and deployment overhead.
📝 Abstract
Extracting structured data from the web is often a trade-off between the brittle nature of manual heuristics and the prohibitive cost of Large Language Models. We introduce AXE (Adaptive X-Path Extractor), a pipeline that rethinks this process by treating the HTML DOM as a tree that needs pruning rather than just a wall of text to be read. AXE uses a specialized"pruning"mechanism to strip away boilerplate and irrelevant nodes, leaving behind a distilled, high-density context that allows a tiny 0.6B LLM to generate precise, structured outputs. To keep the model honest, we implement Grounded XPath Resolution (GXR), ensuring every extraction is physically traceable to a source node. Despite its low footprint, AXE achieves state-of-the-art zero-shot performance, outperforming several much larger, fully-trained alternatives with an F1 score of 88.1% on the SWDE dataset. By releasing our specialized adaptors, we aim to provide a practical, cost-effective path for large-scale web information extraction.