A Generative Approach for Comprehensive Financial Event Extraction at the Document Level

Financial event extraction enables the extraction of comprehensive and accurate information about financial events from documents. This paper explores the current methods for extracting events at the financial document level, which often involve custom-designed networks and processes. We question whether such extensive efforts are truly necessary for this task. Our research is motivated by recent developments in generative event extraction, which have shown success in sentence-level extraction but have yet to be explored for financial document-level extraction. To fill this gap, we propose a generative solution for document-level event extraction, which is more challenging due to the presence of scattered arguments and multiple events. We introduce an encoding scheme to capture entity-to-document level information and a decoding scheme that makes the generative process aware of all relevant contexts. Our results indicate that using our method, a generative-based solution can perform as well as state-of-the-art methods that use a specialized structure for document event extraction, providing an easy-to-use, strong baseline for future research.