PACE: Publisher-Adaptive Content Extraction via Agentic Automation
PACE is an agentic framework that learns publisher-specific extraction configurations from representative pages and user requirements, bridging the gap between general-purpose extractors and manual parsers During training, LLMs analyze page structure and aggregate reusable extraction patterns; at inference, these configurations instantiate a fixed deterministic extractor template with no additional LLM calls Experiments across article-body, metadata, and multimodal extraction show PACE outperfor
Analysis
TL;DR
- PACE is an agentic framework that learns publisher-specific extraction configurations from representative pages and user requirements, bridging the gap between general-purpose extractors and manual parsers
- During training, LLMs analyze page structure and aggregate reusable extraction patterns; at inference, these configurations instantiate a fixed deterministic extractor template with no additional LLM calls
- Experiments across article-body, metadata, and multimodal extraction show PACE outperforms scalable non-manual baselines while approaching the quality of manually engineered publisher-specific parsers
- The framework enables automated, publisher-specific extraction of article text, metadata, images, and tables for LLM-ready page representations at scale
Why It Matters
Web content extraction is a critical bottleneck in building reliable LLM data pipelines, and PACE directly addresses the longstanding tradeoff between accuracy and scalability. By automating the creation of publisher-specific parsers through agentic configuration learning, it reduces the substantial human effort traditionally required while maintaining high extraction quality. This has direct implications for anyone building large-scale data ingestion systems for LLM training or RAG pipelines.
Technical Details
- Agentic configuration learning: PACE uses LLMs during a training phase to analyze representative pages from each publisher, understand their structural patterns, and distill reusable extraction configurations
- Deterministic inference: Learned configurations are compiled into a fixed deterministic extractor template, eliminating per-page LLM calls at inference time and enabling high-throughput, low-latency extraction
- Multi-target extraction: The framework supports extraction beyond article body text, including metadata, images, and tables, producing comprehensive LLM-ready page representations
- Evaluation scope: Experiments cover article-body extraction, metadata extraction, and multimodal extraction, comparing against both scalable non-manual baselines and manually engineered publisher-specific parsers
Industry Insight
- The agentic-then-deterministic paradigm demonstrated by PACE offers a reusable blueprint for other domains where LLM flexibility is needed during development but cost and latency constraints demand deterministic execution at scale
- As LLM data pipelines continue to grow, automated publisher adaptation will become increasingly valuable; organizations investing in this approach can reduce manual parser maintenance costs while improving extraction coverage
- The ability to extract structured multimodal content (images, tables, metadata) alongside article text positions PACE as a step toward fully automated, end-to-end web-to-LLM-ready-data pipelines
Disclaimer: The above content is generated by AI and is for reference only.