Summary of Towards Efficient Resume Understanding: a Multi-granularity Multi-modal Pre-training Approach, by Feihu Jiang et al.
Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach
by Feihu Jiang, Chuan Qin, Jingshuai Zhang, Kaichun Yao, Xi Chen, Dazhong Shen, Chen Zhu, Hengshu Zhu, Hui Xiong
First submitted to arxiv on: 13 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper proposes a novel approach for efficient resume understanding, leveraging pre-trained document understanding models and hierarchical relations within resumes. The authors introduce a layout-aware multi-modal fusion transformer to encode resume segments with integrated textual, visual, and layout information. They also design self-supervised tasks to pre-train this module using unlabeled resumes, followed by fine-tuning via a sequence labeling task to extract structured information. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed ERU model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding resumes. Right now, there are many ways for computers to understand text, but they often get stuck when it comes to understanding things that are laid out in a specific way, like a resume. The authors want to make computers better at this by using special kinds of AI models that can learn from lots and lots of resumes without being shown what the answers should be. They also designed a new way for these models to understand different parts of a resume, like text, pictures, and layout. This helps computers extract useful information from resumes more accurately. |
Keywords
» Artificial intelligence » Fine tuning » Multi modal » Self supervised » Transformer
