A Unified Model for Financial Event Classification, Detection and Summarization
Abstract
There is massive amount of news on financial events every day. In this paper, we present a unified model for detecting, classifying and summarizing financial events. This model exploits a multi-task learning approach, in which a pre-trained BERT model is used to encode the news articles, and the encoded information are shared by event type classification, detection and summarization tasks. For event summarization, we use a Transformer structure as the decoder. In addition to the input document encoded by BERT, the decoder also utilizes the predicted event type and cluster information, so that it can focus on the specific aspects of the event when generating summary. Our experiments show that our approach outperforms other methods.
Cite
Text
Li and Zhang. "A Unified Model for Financial Event Classification, Detection and Summarization." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/644Markdown
[Li and Zhang. "A Unified Model for Financial Event Classification, Detection and Summarization." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/li2020ijcai-unified/) doi:10.24963/IJCAI.2020/644BibTeX
@inproceedings{li2020ijcai-unified,
title = {{A Unified Model for Financial Event Classification, Detection and Summarization}},
author = {Li, Quanzhi and Zhang, Qiong},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {4668-4674},
doi = {10.24963/IJCAI.2020/644},
url = {https://mlanthology.org/ijcai/2020/li2020ijcai-unified/}
}