Reasoning models for adaptive information extraction in scientific documents

Abstract

The exponential growth of scientific publishing has made accurate and efficient metadata extraction a crucial task for enabling search, retrieval, and knowledge management in scholarly communication. However, the diversity of journal formats and evolving publication practices pose significant challenges to traditional rule-based extraction systems. This article explores reasoning models as a foundation for adaptive metadata extraction in scientific documents. It examines the strengths and limitations of rule-based, case-based, probabilistic, and hybrid reasoning approaches, showing how they can be integrated to support robust and flexible extraction processes. An adaptive workflow is described in which annotated examples guide the generation of extraction rules that are refined through iterative reasoning strategies. The article argues that reasoning models not only improve the accuracy and scalability of metadata extraction but also provide interpretability, adaptability, and resilience to variations in document structures. Future directions point toward hybrid systems that combine reasoning with advances in machine learning and natural language processing, creating intelligent infrastructures for the dynamic landscape of scientific publishing.