文档索引的未来：GPT与Donut革新目录处理
The future of document indexing: GPT and Donut revolutionize table of content processing
AI总结：
针对工业规范文档人工提取结构化信息效率低的问题，提出结合Donut与GPT-3.5 Turbo的目录自动提取结构化方法，分别取得85%、89%的准确率，革新文档索引流程。
摘要：
工业项目严重依赖冗长复杂的规范文档，繁琐的结构化信息人工提取已成为主要瓶颈。本文提出一种自动化该流程的创新方法，依托两款前沿AI模型的能力：Donut（一款无需OCR即可直接从扫描文档中提取信息的模型），以及OpenAI GPT-3.5 Turbo（一款性能强大的大语言模型）。所提方法首先从建筑规范文档中获取目录（ToCs），随后将目录文本结构化为JSON数据。该方法取得了显著准确率，在有效整理目录方面，Donut达到85%，GPT-3.5 Turbo达到89%。这一里程碑式的成果代表了文档索引领域的重大飞跃，证明了AI在各类文档中自动化执行信息提取任务的巨大潜力，可提升效率并释放各行业的关键资源。
英文摘要：
Industrial projects rely heavily on lengthy, complex specification documents, making tedious manual extraction of structured information a major bottleneck. This paper introduces an innovative approach to automate this process, leveraging the capabilities of two cutting-edge AI models: Donut, a model that extracts information directly from scanned documents without OCR, and OpenAI GPT-3.5 Turbo, a robust large language model. The proposed methodology is initiated by acquiring the table of contents (ToCs) from construction specification documents and subsequently structuring the ToCs text into JSON data. Remarkable accuracy is achieved, with Donut reaching 85% and GPT-3.5 Turbo reaching 89% in effectively organizing the ToCs. This landmark achievement represents a significant leap forward in document indexing, demonstrating the immense potential of AI to automate information extraction tasks across diverse document types, boosting efficiency and liberating critical resources in various industries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
