Improve dataset card: Add task categories, tags, and link to survey paper

This PR updates the dataset card for `GeoExpand` and `GeoSynth` to improve its discoverability and provide richer context.

Key changes include:
- **Adding `image-text-to-text`** to the `task_categories` metadata, as specified, alongside the existing `visual-question-answering`.
- **Adding relevant `tags`**: `mathematical-reasoning`, `geometry-problem-solving`, and `multimodal-reasoning` to better categorize the dataset.
- **Referencing the related survey paper**: [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936), where these datasets are discussed, and its accompanying GitHub repository: [majianz/gps-survey](https://github.com/majianz/gps-survey).
- **Clarifying the dataset card title**: to `GeoGeo: GeoExpand & GeoSynth` for better clarity.

These changes help users understand the dataset's purpose and its place within the broader AI research landscape.

README.md

@@ -1,21 +1,28 @@
 ---
-license: mit
-task_categories:
-- visual-question-answering
 language:
 - en
-pretty_name: GeoExpand & GeoSynth
+license: mit
 size_categories:
 - 10K<n<100K
+task_categories:
+- visual-question-answering
+- image-text-to-text
+pretty_name: GeoExpand & GeoSynth
+tags:
+- mathematical-reasoning
+- geometry-problem-solving
+- multimodal-reasoning
 ---
 
+# GeoGeo: GeoExpand & GeoSynth
 
-# GeoGeo
+This repository contains the **GeoExpand** and **GeoSynth** datasets, originally introduced in the paper [Enhancing the Geometric Problem-Solving Ability of Multimodal LLMs via Symbolic-Neural Integration](https://arxiv.org/pdf/2504.12773).
 
-Paper: [Enhancing the Geometric Problem-Solving Ability of Multimodal LLMs via Symbolic-Neural Integration](https://arxiv.org/pdf/2504.12773)
+The datasets are designed to enhance and evaluate the geometric problem-solving capabilities of multimodal large language models.
 
-GitHub: [ycpNotFound/GeoGen: A pipeline for the automatic construction of geometry problems along with step-by-step solutions.](https://github.com/ycpNotFound/GeoGen)
+GitHub Repository: [ycpNotFound/GeoGen](https://github.com/ycpNotFound/GeoGen)
 
+These datasets are also referenced and contextualized in the survey paper [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936), which provides a comprehensive overview of the field. The survey's reading list is maintained on its GitHub repository: [majianz/gps-survey](https://github.com/majianz/gps-survey).
 
-- GeoExpand includes 45,526 Q&A samples, generated from 4849 images in total of Geometry3K and PGPS9K.
-- GeoSynth includes 62,868 Q&A samples, which one diagram for one Q&A each.
+-   **GeoExpand** includes 45,526 Q&A samples, generated from 4849 images in total of Geometry3K and PGPS9K.
+-   **GeoSynth** includes 62,868 Q&A samples, with one diagram for one Q&A each.