arXiv — NLP / Computation & Language · · 3 min read

PACE: Publisher-Adaptive Content Extraction via Agentic Automation

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Computer Science > Computation and Language

arXiv:2608.27466 (cs)
[Submitted on 20 Jul 2026]

Title:PACE: Publisher-Adaptive Content Extraction via Agentic Automation

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Abstract: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.
We 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.
Experiments 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.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.27466 [cs.CL]
  (or arXiv:2608.27466v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.27466
arXiv-issued DOI via DataCite

Submission history

From: Zhanlin Liu [view email]
[v1] Mon, 20 Jul 2026 18:12:08 UTC (1,510 KB)
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