Information Extraction (IE) encompasses a diverse array of tasks in Natural Language Processing (NLP), including Named Entity Recognition (NER), Entity Linking (EL), and Attribute Value Extraction (AVE), all of which aim to derive structured representations from unstructured text. Despite their shared goals, these tasks are often studied in isolation, leading to redundant methods and fragmented advances. This work introduces a unified framework for IE centered on the concept of a Span: a contiguous sequence of tokens annotated with one or more semantic labels. By adopting spans as the foundational unit of analysis, we unify a broad class of IE tasks under a common formulation. We formalize key dimensions of this span-oriented paradigm: task formulation via span identification and classification, evaluation through boundary- and label-sensitive metrics, representation strategies including token-to-span encodings, architectural components tailored to span extraction, and modeling strategies grounded in pre-trained language models. We also identify persistent challenges, such as boundary ambiguity and context-label mismatch, that cross tasks. Through this unified lens, we synthesize and standardize a field long divided by task-specific assumptions. The result is a cohesive framework that supports cross-task generalization, standardized evaluation, and principled model design, which lays the foundation for future advances in both spanbased and general-purpose information extraction systems.
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