| Challenge: | Existing corpora for financial narrative summarisation exists in English but there is a significant lack of financial text resources in the French language. |
| Approach: | They propose to use natural language processing to analyse financial documents to find the best summarisation methods. |
| Outcome: | The proposed dataset is the first to provide a comprehensive set of financial text written in French. |
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| Challenge: | Recent advances in neural computing and word embeddings for semantic processing open many new applications areas which had been left unaddressed due to inadequate language understanding capacity. |
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MultiFin: A Dataset for Multilingual Financial NLP (2023.findings-eacl)
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| Challenge: | Multilingual models are needed to process financial text, which is produced across the world and requires a large dataset. |
| Approach: | They propose to annotate a publicly available financial dataset using a hierarchical label structure and an annotation schema based on a real-world application. |
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
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| Challenge: | a predominantly German corpus of financial documents is available for the first time . financial text is characterized by a unique vocabulary with implications including sentiment analysis . |
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A French Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News (2020.lrec-1)
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| Challenge: | Strict regulatory regimes mandate financial institutions to rigorously monitor their customers' financial activities. |
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Proceedings of the Second Workshop on Economics and Natural Language Processing (D19-51)
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SEDAR: a Large Scale French-English Financial Domain Parallel Corpus (2020.lrec-1)
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| Challenge: | Existing approaches for neural machine translation use small amount of data or monolingual data. |
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FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)
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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie
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FinQA: A Dataset of Numerical Reasoning over Financial Data (2021.emnlp-main)
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Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, William Yang Wang
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