Update dataset card for DL4GPS survey reading list

This PR updates the dataset card to accurately reflect the content of this repository, which serves as a continuously updated reading list for the paper "[A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936)".

It replaces the previous content related to `OlymMATH` with details specific to the survey and its GitHub repository, and updates the task category as instructed.

README.md

@@ -1,49 +1,44 @@
 ---
 language:
-- zh
 - en
 license: mit
 task_categories:
-- question-answering
-configs:
-- config_name: en-hard
-  data_files:
-  - split: test
-    path: data/OlymMATH-EN-HARD.jsonl
-- config_name: zh-hard
-  data_files:
-  - split: test
-    path: data/OlymMATH-ZH-HARD.jsonl
-- config_name: en-easy
-  data_files:
-  - split: test
-    path: data/OlymMATH-EN-EASY.jsonl
-- config_name: zh-easy
-  data_files:
-  - split: test
-    path: data/OlymMATH-ZH-EASY.jsonl
+- text-generation
+tags:
+- survey
+- mathematical-reasoning
+- geometry
+- deep-learning
 ---
 
-# Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models
+# A Survey of Deep Learning for Geometry Problem Solving
 
-This is the official huggingface repository for [Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models](https://arxiv.org/abs/2503.21380) by [Haoxiang Sun](https://github.com/CoderBak), Yingqian Min, Zhipeng Chen, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang, Lei Fang, and Ji-Rong Wen.
+This repository is the reading list accompanying the paper [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936).
 
-We have also released the [OlymMATH-eval](https://hf.co/datasets/RUC-AIBOX/OlymMATH-eval) dataset on HuggingFace 🤗, together with a data visualization tool [OlymMATH-demo](https://hf.co/spaces/RUC-AIBOX/OlymMATH-demo), currently available in HuggingFace Spaces.
+It aims to provide a comprehensive and practical reference for deep learning applications in geometry problem solving. The repository is continuously updated and organized to allow searching for corresponding papers by year.
 
-You can find more information on [GitHub](https://github.com/RUCAIBox/OlymMATH).
+**Paper Abstract:**
+Recent years have seen rapid advancements in deep learning, especially multimodal large language models, sparking a research boom in geometry problem solving—a critical area for mathematical reasoning, AI assessment, and multimodal capabilities. This paper surveys deep learning applications in this field, summarizing relevant tasks, reviewing deep learning methods, analyzing evaluation metrics, and discussing challenges and future directions. Our goal is to offer a comprehensive, practical reference to foster further progress. We maintain a continuously updated paper list on GitHub.
 
-## Citation
+**GitHub Repository:** [https://github.com/majianz/gps-survey](https://github.com/majianz/gps-survey)
 
-If you find this helpful in your research, please give a 🌟 to our repo and consider citing
+The reading list includes papers categorized by:
+*   Surveys
+*   Tasks and Datasets (Fundamental, Core, Composite, and Other Geometry Tasks)
+*   Architectures
+*   Methods
+*   Related Surveys
 
-```
-@misc{sun2025challengingboundariesreasoningolympiadlevel,
-      title={Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models}, 
-      author={Haoxiang Sun and Yingqian Min and Zhipeng Chen and Wayne Xin Zhao and Zheng Liu and Zhongyuan Wang and Lei Fang and Ji-Rong Wen},
+### Citation
+If you find this survey helpful in your research, please consider citing the paper:
+
+```bibtex
+@misc{ma2025survey,
+      title={A Survey of Deep Learning for Geometry Problem Solving}, 
+      author={Jianzhe Ma and Tianyi Li and Haoran Wei and Yingqian Min and Lei Fang and Ji-Rong Wen},
       year={2025},
-      eprint={2503.21380},
+      eprint={2507.11936},
       archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2503.21380}, 
+      primaryClass={cs.CL}
 }
 ```