Keyword-based Annotation of Visually-Rich Document Content for Trend and Risk Analysis Using Large Language Models
Giuseppe Gallipoli, Simone Papicchio, Lorenzo Vaiani, Luca Cagliero, Arianna Miola, Daniele Borghi
Abstract
In the banking and finance sectors, members of the business units focused on Trend and Risk Analysis daily process internal and external visually-rich documents including text, images, and tables. Given a facet (i.e., topic) of interest, they are particularly interested in retrieving the top trending keywords related to it and then use them to annotate the most relevant document elements (e.g., text paragraphs, images or tables). In this paper, we explore the use of both open-source and proprietary Large Language Models to automatically generate lists of facet-relevant keywords, automatically produce free-text descriptions of both keywords and multimedia document content, and then annotate documents by leveraging textual similarity approaches. The preliminary results, achieved on English and Italian documents, show that OpenAI GPT-4 achieves superior performance in keyword description generation and multimedia content annotation, while the open-source Meta AI Llama2 model turns out to be highly competitive in generating additional keywords.- Anthology ID:
- 2024.finnlp-1.13
- Volume:
- Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Chung-Chi Chen, Xiaomo Liu, Udo Hahn, Armineh Nourbakhsh, Zhiqiang Ma, Charese Smiley, Veronique Hoste, Sanjiv Ranjan Das, Manling Li, Mohammad Ghassemi, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
- Venue:
- FinNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 130–136
- Language:
- URL:
- https://aclanthology.org/2024.finnlp-1.13
- DOI:
- Cite (ACL):
- Giuseppe Gallipoli, Simone Papicchio, Lorenzo Vaiani, Luca Cagliero, Arianna Miola, and Daniele Borghi. 2024. Keyword-based Annotation of Visually-Rich Document Content for Trend and Risk Analysis Using Large Language Models. In Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing, pages 130–136, Torino, Italia. Association for Computational Linguistics.
- Cite (Informal):
- Keyword-based Annotation of Visually-Rich Document Content for Trend and Risk Analysis Using Large Language Models (Gallipoli et al., FinNLP 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.finnlp-1.13.pdf