Kang, J. and Patel, M. and Agrawal, A. and Sevitha, S. and Srinivasa, R. and Bellato, S. and Kumar, M. Anand and Tsang, N. and El-Haj, M. (2024) Advancements in Financial Document Structure Extraction : Insights from Five Years of FinTOC (2019-2023). In: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 :. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 . IEEE Computer Society Press, Los Alamitos, CA, USA, pp. 2839-2844. ISBN 9798350324457
Full text not available from this repository.Abstract
In this comprehensive paper, we present a detailed overview of the Financial Table Of Content extraction shared task series, FinTOC, conducted over a span of five years from 2019 to 2023. This paper serves as a retrospective analysis of the key developments in the field of financial document structure extraction. The FinTOC series, hosted within the framework of the Financial Narrative Processing (FNP) workshop, has been instrumental in shaping the landscape of Natural Language Processing (NLP) in the financial domain. Our analysis delves into the diverse methodologies proposed by participants across all editions, shedding light on the innovative strategies employed to tackle the intricate challenge of extracting structured information from financial documents. We explore the evolution of techniques, from traditional rule-based approaches to cutting-edge deep learning models, showcasing the dynamic nature of NLP advancements. Furthermore, our study investigates the introduction of multilingual datasets by the organizers, highlighting the importance of cross-lingual analysis in financial document processing. We also examine the contributions made by participants in augmenting the training data with external sources, showcasing the collaborative spirit of the NLP community in enhancing the quality and size of the shared training dataset.