PACE: Publisher-Adaptive Content Extraction via Agentic Automation

PACE is an agentic framework that learns publisher‑specific extraction configurations from representative web pages and user requirements. During training it employs LLMs to analyze page structure and gather reusable extraction patterns, then creates a deterministic extractor template for inference that eliminates the need for further LLM calls. Experiments on article bodies, metadata, images, and tables show that PACE surpasses scalable non‑manual baselines and approaches the quality of manually engineered publisher‑specific parsers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

• llms
• agents
• multimodal