English(EN) SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning

新的SCRIBES框架使用可重用脚本提取Web数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-19 04:00

研究人员开发了SCRIBES，一个强化学习框架，旨在从半结构化的Web内容（如HTML表格和列表）中提取结构化信息。该方法通过利用同一网站内网页之间的布局相似性作为奖励信号来生成可重用的提取脚本，从而避免了资源密集型的每页LLM推理。该框架通过在CommonCrawl数据上的合成标注进行训练而得到改进，在脚本质量上优于现有方法，并提高了GPT-4o等模型下游问答的准确性。 AI

影响能够更高效、可扩展地从Web中提取结构化数据，可能改进下游AI应用。

排序理由该集群描述了一篇详细介绍新颖数据提取框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇详细介绍新颖数据提取框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 47 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Shicheng Liu, Kai Sun, Lisheng Fu, Xilun Chen, Xinyuan Zhang, Zhaojiang Lin, Rulin Shao, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong · 2026-08-19 04:00

SCRIBES: 基于强化学习的万亿级脚本式半结构化数据提取

arXiv:2510.01832v2 Announce Type: replace Abstract: Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complicates usage, and reliably extracting structured information from them remains cha…

本文如何被排名

报道来源 [1]

SCRIBES: 基于强化学习的万亿级脚本式半结构化数据提取

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