DLEE: a dataset for Chinese document-level legal event extraction

Event extraction (EE) is capable of providing essential information to facilitate comprehension of legal cases by identifying event types and extracting corresponding arguments from legal case documents. In the legal field, events are often presented in the form of document, with arguments scattered across multiple sentences, which means that legal EE at the document level is needed to better capture the complete event. However, the existing legal EE datasets mainly focused on event extraction at the sentence level, with little attention given to the document level. Obviously, it put the development of document-level event extraction (DEE) in the legal field at a disadvantage. To address this challenge, we proposed DLEE, the first DEE dataset in the legal field with two distinctive features: (1) Document-level Semi-automated Annotation, ensuring effective annotation with high quality. (2) Large-scale and Fine-grained coverage, comprising 10,014 events and 99,423 arguments. Finally, we assessed the performance of commonly used DEE baseline models on DLEE. It revealed that the DLEE is an open question, and further attention is needed for the improvement of the models’ performance.