Abstract: This paper proposes a new task regarding event reason extraction from document-level texts. Unlike the previous causality detection task, we do not assign target events in the text, but only provide structural event descriptions, and such settings accord more with practice scenarios. Moreover, we annotate a large dataset {F}in{R}eason for evaluation, which provides {R}easons annotation for {F}inancial events in company announcements. This task is challenging because the cases of multiple-events, multiple-reasons, and implicit-reasons are included. In total, {F}in{R}eason contains 8,794 documents, 12,861 financial events and 11,006 reason spans. We also provide the performance of existing canonical methods in event extraction and machine reading comprehension on this task. The results show a 7 percentage point F1 score gap between the best model and human performance, and existing methods are far from resolving this problem.