Abstract:  Financial announcement is the carrier for enterprises to publicly inform the society of major financial events, and its information is of great significance to financial practitioners. However, financial events have the characteristics of strong argument specialization and high dispersion, and traditional event extraction methods are difficult to achieve accurate extraction. Therefore an event extraction method combining the local features of paragraphs and the global features of documents is proposed. This method first segments the financial announcement document, and then uses all the paragraphs in parallel Fin-BERT(Financial Bidirectional Encoder Representation from Transformers ) Pre training model, convolutional neural network and self attention mechanism to obtain local features of documents. Then Bi LSTM(Bi directional Long Short Term Memory) is used to learn the semantic information of the whole document to obtain the global features of the document. Finally, the local features of the paragraph and the global features of the document are fused to output event arguments and event types. A series of experiments are carried out on the financial open data set chfinann. The experimental results show that the method achieves an average F1 value of 80. 2%, which is better than the baseline model, and proves the effectiveness of the method. 

Key words: event extraction, financial announcements, scattering of event elemen