Omkar2118/datasets huggingface dataset:
EDGAR10-Q Dataset

This dataset is built from 10-Q documents (Quarterly Reports) of publicly listed companies on the SEC.

Introducing paper: Context-NER : Contextual Phrase Generation at Scale
ChFinAnn

Ten years (2008-2018) ChFinAnn documents and human-summarized event knowledge bases to conduct the DS-based event labeling.
Five event types included: Equity Freeze (EF), Equity Repurchase (ER), Equity Underweight (EU), Equity Overweight (EO) and Equity Pledge (EP), which belong to major events required to be disclosed by the regulator and may have a huge impact on the company value.
To ensure the labeling quality, the authors set constraints for matched document-record pairs.

There are 32, 040 documents in total, and this number is ten times larger than 2, 976 of DCFEE and about 53 times larger than 599 of ACE 2005.
These documents are divided into train, development, and test set with the proportion of 8 : 1 : 1 based on the time order.
This DS-generated data are pretty good, achieving high precision and acceptable recall.
In later experiments, the authors directly employ the automatically generated test set for evaluation due to its much broad coverage.

Introducing paper: Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction