Move abstract section to top of README

README.md

@@ -43,10 +43,10 @@ default_config_name: UltraData-Math-L3-Conversation-Synthetic
 <a href="https://huggingface.co/datasets/openbmb/UltraData-Math">🤗 Dataset</a> | <a href="https://github.com/UltraData-OpenBMB/UltraData-Math">💻 Source Code</a> | <a href="https://huggingface.co/datasets/openbmb/UltraData-Math/blob/main/README_ZH.md">🇨🇳 中文 README</a>
 </p>
 
-## 📚 Introduction
-
 ***UltraData-Math*** is a large-scale, high-quality mathematical pre-training dataset totaling **290B+ tokens** across three progressive tiers—**L1** (170.5B tokens web corpus), **L2** (33.7B tokens quality-selected), and **L3** (88B tokens multi-format refined)—designed to systematically enhance mathematical reasoning in LLMs. It has been applied to the mathematical pre-training of the [MiniCPM Series](https://huggingface.co/collections/openbmb/minicpm4) models.
 
+## 📚 Introduction
+
 High-quality pre-training data is crucial for enhancing the mathematical reasoning capabilities of large language models (LLMs). However, existing mathematical pre-training data construction schemes have the following shortcomings:
 
 - **HTML Parsing**: General parsers (such as trafilatura, readability) are mainly designed for news/article parsing, lacking specialized processing for mathematical formulas and other content, often leading to formula structure destruction or loss; meanwhile, mathematical discussions on forum-like pages are difficult to extract completely.

README_ZH.md

@@ -8,10 +8,10 @@
 <a href="https://huggingface.co/datasets/openbmb/UltraData-Math">🤗 数据集</a> | <a href="https://github.com/UltraData-OpenBMB/UltraData-Math">💻 源代码</a> | <a href="README.md">🇺🇸 English README</a>
 </p>
 
-## 📚 简介
-
 ***UltraData-Math*** 是一个面向数学推理的大规模高质量预训练数据集，总计 **290B+ tokens**，涵盖三个递进层级——**L1**（170.5B tokens 网页语料）、**L2**（33.7B tokens 质量精选）、**L3**（88B tokens 多格式精炼），旨在系统性提升大语言模型的数学推理能力。已应用于 [MiniCPM 系列](https://huggingface.co/collections/openbmb/minicpm-4-6841ab29d180257e940baa9b) 模型的数学预训练。
 
+## 📚 简介
+
 高质量预训练数据对提升大语言模型的数学推理能力至关重要。然而，现有数学预训练数据构建方案存在以下不足：
 
 - **HTML 解析层面**：通用提取器（如 trafilatura、readability）主要面向新闻/文章场景设计，对数学公式等内容缺乏专门处理，常导致公式结构破坏或丢失；同时论坛类页面的数学讨论部分，难以完整提取。