Summary of Aceparse: a Comprehensive Dataset with Diverse Structured Texts For Academic Literature Parsing, by Huawei Ji and Cheng Deng and Bo Xue and Zhouyang Jin and Jiaxin Ding and Xiaoying Gan and Luoyi Fu and Xinbing Wang and Chenghu Zhou
AceParse: A Comprehensive Dataset with Diverse Structured Texts for Academic Literature Parsing
by Huawei Ji, Cheng Deng, Bo Xue, Zhouyang Jin, Jiaxin Ding, Xiaoying Gan, Luoyi Fu, Xinbing Wang, Chenghu Zhou
First submitted to arxiv on: 16 Sep 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 The paper introduces AceParse, a comprehensive dataset designed to support the parsing of various structured texts in academic literature. The dataset covers a wide range of text structures, including formulas, tables, lists, algorithms, and sentences with embedded mathematical expressions. The authors fine-tuned a multimodal model called AceParser, which outperforms previous state-of-the-art models by 4.1% in terms of F1 score and by 5% in Jaccard Similarity. This paper demonstrates the potential of multimodal models for parsing academic literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better process data from academic papers, like formulas and tables. The researchers created a special dataset called AceParse that can help machines read this type of information. They also made a new model called AceParser that does an even better job at reading these texts than previous models did. |
Keywords
» Artificial intelligence » F1 score » Parsing
