Align dataset card with 'A Survey of Deep Learning for Geometry Problem Solving' reading list

This pull request completely updates the dataset card to accurately reflect the contents 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).

The previous content describing the `allenai/lila` dataset has been replaced with information relevant to this survey. This includes:
- Linking to the correct Hugging Face paper page for the survey.
- Linking to the GitHub repository which functions as the dynamic reading list.
- Updating the license in the metadata from `CC-BY-4.0` to `MIT`, as specified in the source GitHub repository.
- Adding `text-generation` as the task category, as per the task instructions, along with relevant tags such as `mathematical-reasoning`, `geometry`, `survey`, `deep-learning`, `reading-list`, and `bibliography` for improved discoverability.

README.md

@@ -1,45 +1,47 @@
 ---
-license: cc-by-4.0
+license: mit
+task_categories:
+- text-generation
+tags:
+- mathematical-reasoning
+- geometry
+- survey
+- deep-learning
+- reading-list
+- bibliography
 ---
 
-## Dataset Description
+# Deep Learning for Geometry Problem Solving (DL4GPS)
 
-- **Repository:** [allenai/lila](https://github.com/allenai/lila)
-- **Paper:** [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392.pdf)
-- **Point of Contact:** [Matthew Finlayson](https://mattf1n.github.io/), [Sean Welleck](https://wellecks.com/)
+This repository serves as a continuously updated reading list for the survey paper:
+[**A Survey of Deep Learning for Geometry Problem Solving**](https://huggingface.co/papers/2507.11936).
 
-# Lila: A Unified Benchmark for Mathematical Reasoning
+## Paper Abstract
 
+Geometry problem solving is a key area of mathematical reasoning, which is widely involved in many important fields such as education, mathematical ability assessment of artificial intelligence, and multimodal ability assessment. In recent years, the rapid development of deep learning technology, especially the rise of multimodal large language models, has triggered a widespread research boom. This paper provides a survey of the applications of deep learning in geometry problem solving, including (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; (iii) a detailed analysis of evaluation metrics and methods; and (iv) a critical discussion of the current challenges and future directions that can be explored. Our goal is to provide a comprehensive and practical reference of deep learning for geometry problem solving to promote further developments in this field. We create a continuously updated list of papers on GitHub: this https URL .
 
-- **Homepage: https://lila.apps.allenai.org/** 
-- **Repository: https://github.com/allenai/lila** 
-- **Paper: https://aclanthology.org/2022.emnlp-main.392.pdf** 
+## Reading List and GitHub Repository
 
+The full and continuously updated reading list, including papers categorized by tasks, datasets, architectures, and methods, is maintained on the associated GitHub repository:
+[https://github.com/majianz/gps-survey](https://github.com/majianz/gps-survey)
 
-### Licensing Information
+This resource aims to provide a comprehensive and practical reference for deep learning in geometry problem solving to foster further developments in this field.
 
-Creative Commons Attribution 4.0 International
+## Licensing Information
 
-### Citation Information
+This reading list and its contents are licensed under the MIT License.
 
-Cite this dataset and the source datasets (see [sources.bib](https://github.com/allenai/Lila/blob/main/sources.bib)). 
+## Citation
 
-```bib
-@INPROCEEDINGS{Mishra2022Lila,
-  author = {
-    Swaroop Mishra 
-      and Matthew Finlayson 
-      and Pan Lu 
-      and Leonard Tang 
-      and Sean Welleck 
-      and Chitta Baral 
-      and Tanmay Rajpurohit 
-      and Oyvind Tafjord 
-      and Ashish Sabharwal 
-      and Peter Clark 
-      and Ashwin Kalyan},
-  title = {Lila: A Unified Benchmark for Mathematical Reasoning},
-  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
-  year = {2022}
+If you find this survey and reading list useful, please cite the paper:
+
+```bibtex
+@misc{ma2025survey,
+      title={A Survey of Deep Learning for Geometry Problem Solving},
+      author={Jianzhe Ma and Hao Zhou and Jingnan Xia and Xianzhe Zhang and Xinyuan Chen and Zhi Chen and Hongyu Zang and Peng Wang and Xiang Li},
+      year={2025},
+      eprint={2507.11936},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI}
 }
-```
+```