Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- Automated Peer Reviewing
+- SFT
+- Dataset
+---
+
+## Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
+
+Paper Link: https://arxiv.org/abs/2311.09278
+
+Project Page: https://ecnu-sea.github.io/
+
+
+## Dataset Details
+
+Each dataset contains four types of files as follows:
+
+- **paper_raw_pdf:** Original paper in PDF format 
+- **paper_nougat_mmd:** The mmd files after parsed by [Nougat](https://github.com/facebookresearch/nougat)
+- **review_raw_txt:** Crawled raw review text
+- **review_json:** The processed review JSON file, including “Decision”, “Meta Review”, and for each review, “Summary”, “Strengths”, “Weaknesses”, “Questions”, “Soundness”, “Presentation”, “Contribution”, “Confidence”, and “Rating”.
+
+
+## Dataset Sources
+
+We crawl the latest papers and their corresponding reviews from [OpenReview](https://openreview.net), including NeurIPS-2023 and ICLR-2024.
+
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+If you find our paper or models helpful, please consider cite as follows:
+
+```bibtex
+@misc{yu2024sea,
+  title={Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis}, 
+  author={Jianxiang Yu and Zichen Ding and Jiaqi Tan and Kangyang Luo and Zhenmin Weng and Chegnhua Gong and Long Zeng and Renjing Cui and Chengcheng Han and Qiushi Sun and Zhiyong Wu and Yunshi Lan and Xiang Li},
+  year={2024},
+  eprint={2406.26456},
+  archivePrefix={arXiv},
+  primaryClass={cs.AI}
+}
+```