A legal document summarization framework is described, but the experiments use four non-legal summarization datasets and generic equations, so the claimed judicial efficiency improvements are not established.
The future of document indexing: GPT and Donut revolutionize table of content processing
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
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.
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Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection
A legal document summarization framework is described, but the experiments use four non-legal summarization datasets and generic equations, so the claimed judicial efficiency improvements are not established.