Summary of Vibertgrid Bilstm-crf: Multimodal Key Information Extraction From Unstructured Financial Documents, by Furkan Pala et al.
ViBERTgrid BiLSTM-CRF: Multimodal Key Information Extraction from Unstructured Financial Documents
by Furkan Pala, Mehmet Yasin Akpınar, Onur Deniz, Gülşen Eryiğit
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to adapt a multimodal transformer, specifically ViBERTgrid, for extracting key information from unstructured financial documents. By incorporating a BiLSTM-CRF layer, the proposed model, ViBERTgrid BiLSTM-CRF, achieves significant improvements in named entity recognition (up to 2 percentage points) on unstructured documents while maintaining its performance on semi-structured documents. The study also releases token-level annotations for the SROIE dataset, paving the way for its use in multimodal sequence labeling models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer program that can read and understand financial reports without being trained specifically for that task. This paper shows how to make such a program better at finding important information like company names and numbers from unstructured documents like PDFs. The new approach uses a special kind of artificial intelligence called ViBERTgrid, which is good at understanding semi-structured documents, and adapts it for use with unstructured documents. This can be useful in many areas, including finance and business. |
Keywords
» Artificial intelligence » Named entity recognition » Token » Transformer
