Summary of Srfund: a Multi-granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding, by Jiefeng Ma et al.
SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding
by Jiefeng Ma, Yan Wang, Chenyu Liu, Jun Du, Yu Hu, Zhenrong Zhang, Pengfei Hu, Qing Wang, Jianshu Zhang
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 research paper presents a new benchmark for form understanding, addressing the limitations of existing datasets. The proposed SRFUND dataset is hierarchically structured and includes five tasks: word to text-line merging, text-line to entity merging, entity category classification, item table localization, and entity-based full-document hierarchical structure recovery. The dataset covers eight languages, including English, Chinese, Japanese, German, French, Spanish, Italian, and Portuguese. SRFUND surpasses traditional local key-value associations by introducing global hierarchical structure dependencies for entity relation prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new benchmark called SRFUND to improve the automation of document processing in form understanding. It’s a hierarchically structured dataset that includes five tasks: merging words into text lines, merging text lines into entities, classifying entity categories, localizing item tables, and recovering hierarchical structures. The dataset covers eight languages and is designed for cross-lingual form understanding. |
Keywords
» Artificial intelligence » Classification
