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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01618 |
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| _version_ | 1866913376141049856 |
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| author | Biswas, Anjanava Talukdar, Wrick |
| author_facet | Biswas, Anjanava Talukdar, Wrick |
| contents | Accurate classification of multi-modal financial documents, containing text, tables, charts, and images, is crucial but challenging. Traditional text-based approaches often fail to capture the complex multi-modal nature of these documents. We propose FinEmbedDiff, a cost-effective vector sampling method that leverages pre-trained multi-modal embedding models to classify financial documents. Our approach generates multi-modal embedding vectors for documents, and compares new documents with pre-computed class embeddings using vector similarity measures. Evaluated on a large dataset, FinEmbedDiff achieves competitive classification accuracy compared to state-of-the-art baselines while significantly reducing computational costs. The method exhibits strong generalization capabilities, making it a practical and scalable solution for real-world financial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01618 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FinEmbedDiff: A Cost-Effective Approach of Classifying Financial Documents with Vector Sampling using Multi-modal Embedding Models Biswas, Anjanava Talukdar, Wrick Information Retrieval Artificial Intelligence Accurate classification of multi-modal financial documents, containing text, tables, charts, and images, is crucial but challenging. Traditional text-based approaches often fail to capture the complex multi-modal nature of these documents. We propose FinEmbedDiff, a cost-effective vector sampling method that leverages pre-trained multi-modal embedding models to classify financial documents. Our approach generates multi-modal embedding vectors for documents, and compares new documents with pre-computed class embeddings using vector similarity measures. Evaluated on a large dataset, FinEmbedDiff achieves competitive classification accuracy compared to state-of-the-art baselines while significantly reducing computational costs. The method exhibits strong generalization capabilities, making it a practical and scalable solution for real-world financial applications. |
| title | FinEmbedDiff: A Cost-Effective Approach of Classifying Financial Documents with Vector Sampling using Multi-modal Embedding Models |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2406.01618 |