{"slug": "pace-publisher-adaptive-content-extraction-via-agentic-automation", "title": "PACE: Publisher-Adaptive Content Extraction via Agentic Automation", "summary": "Researchers introduced PACE, an agentic framework that learns publisher-specific web content extraction configurations from representative pages and user requirements, enabling scalable extraction without additional LLM calls at inference time. In experiments spanning article-body, metadata, and multimodal extraction, PACE outperformed scalable non-manual baselines and approached the quality of manually engineered publisher-specific parsers, achieving stronger extraction of article text, metadata, images, and tables.", "body_md": "arXiv:2608.27466v1 Announce Type: new\nAbstract: Web content extraction is essential for reliable LLM data pipelines, yet existing methods often struggle to jointly satisfy accuracy, scalability, and adaptability. General-purpose extractors can be applied broadly, but they are often brittle on publisher-specific layouts and richer extraction targets such as metadata, images, and tables. Direct LLM-based extraction offers greater flexibility, but incurs substantial cost and latency at scale, while manually engineered publisher-specific parsers can achieve high accuracy but require substantial human effort to build and maintain.\nWe introduce PACE, an agentic framework for learning publisher-specific extraction configurations from representative pages and user requirements. During training, PACE uses LLMs to analyze page structure and aggregate reusable extraction patterns. At inference time, the learned configurations instantiate a fixed deterministic extractor template, enabling scalable extraction without additional LLM calls.\nExperiments spanning article-body, metadata, and multimodal extraction show that PACE outperforms scalable non-manual baselines while approaching the quality of manually engineered publisher-specific parsers. PACE achieves stronger extraction of article text, metadata, images, and tables, demonstrating that agentic configuration learning can automate publisher-specific extraction for LLM-ready page representations beyond article text.", "url": "https://wpnews.pro/news/pace-publisher-adaptive-content-extraction-via-agentic-automation", "canonical_source": "https://arxiv.org/abs/2608.27466", "published_at": "2026-08-31 04:00:00+00:00", "updated_at": "2026-08-31 04:24:10.302536+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "natural-language-processing", "ai-research", "ai-agents"], "entities": ["PACE"], "alternates": {"html": "https://wpnews.pro/news/pace-publisher-adaptive-content-extraction-via-agentic-automation", "markdown": "https://wpnews.pro/news/pace-publisher-adaptive-content-extraction-via-agentic-automation.md", "text": "https://wpnews.pro/news/pace-publisher-adaptive-content-extraction-via-agentic-automation.txt", "jsonld": "https://wpnews.pro/news/pace-publisher-adaptive-content-extraction-via-agentic-automation.jsonld"}}