Abstract
This is an extension from a selected paper from JSAI2019. To extract business contents automatically from financial reports is an important problem in the financial area. Especially, segment names and their explanations are important contents that should be extracted. However, the methods for extracting these types of information from financial reports have not been established. In this study, we aim to develop a practical solution for extracting these types of information. To solve this problem, we developed a manually annotated dataset for the task of extracting the segment names and their explanations of each company from financial reports and then developed a recurrent neural network model to solve this task. Our method using the manually annotated dataset outperformed the baseline methods in the task of extracting segment names and their explanations of each company from annual financial reports. In addition, we experimentally demonstrated that our method can be available for this task even when we have a small training dataset. This work is the first work for applying a machine learning method to the task of extracting segment names and their explanations. The insights from this work should be valuable in the industrial area.
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Acknowledgment
This work was supported in part by JSPS KAKENHI Grant Number JP17J04768.
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Ito, T., Sakaji, H., Izumi, K. (2020). Segment Information Extraction from Financial Annual Reports Using Neural Network. In: Ohsawa, Y., et al. Advances in Artificial Intelligence. JSAI 2019. Advances in Intelligent Systems and Computing, vol 1128. Springer, Cham. https://doi.org/10.1007/978-3-030-39878-1_20
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DOI: https://doi.org/10.1007/978-3-030-39878-1_20
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