  Recent advances in machine learning have significantly impacted the field of
information extraction, with Language Models (LMs) playing a pivotal role in
extracting structured information from unstructured text. Prior works typically
represent information extraction as triplet-centric and use classical metrics
such as precision and recall for evaluation. We reformulate the task to be
entity-centric, enabling the use of diverse metrics that can provide more
insights from various perspectives. We contribute to the field by introducing
Structured Entity Extraction and proposing the Approximate Entity Set OverlaP
(AESOP) metric, designed to appropriately assess model performance. Later, we
introduce a new model that harnesses the power of LMs for enhanced
effectiveness and efficiency by decomposing the extraction task into multiple
stages. Quantitative and human side-by-side evaluations confirm that our model
outperforms baselines, offering promising directions for future advancements in
structured entity extraction.
