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README.md

@@ -1,19 +1,86 @@
 ---
-dataset_info:
-  features:
-  - name: article
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 668642138
-    num_examples: 20000
-  download_size: 320970591
-  dataset_size: 668642138
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: mit
+task_categories:
+- text-generation
+- summarization
+language:
+- en
+size_categories:
+- 10K<n<100K
 ---
+
+# ArXiv Summarization Dataset - 20K Preprocessed
+
+A preprocessed dataset of 20,000 ArXiv papers with their full articles and abstracts, designed for abstract generation and summarization tasks.
+
+## Dataset Description
+
+This dataset contains 20,000 ArXiv papers that have been filtered and preprocessed to ensure quality for training summarization models. Each example contains the full article text and its corresponding abstract.
+
+## Dataset Structure
+
+The dataset has the following structure:
+
+- **article**: The full text of the ArXiv paper
+- **abstract**: The abstract/summary of the paper
+
+## Dataset Statistics
+
+- **Total Papers**: 20,000
+- **Article Word Count**:
+  - Mean: 5,875.84 words
+  - Median: 5,217 words
+  - Range: 2,000 - 14,998 words
+- **Abstract Word Count**:
+  - Mean: 179.86 words
+  - Median: 166 words
+  - Range: 50 - 500 words
+- **Length Ratio** (article/abstract):
+  - Mean: 36.00
+  - Median: 32.43
+  - Range: 5.01 - 99.98
+
+## Filtering Criteria
+
+The dataset was filtered using the following criteria:
+- Minimum article words: 2,000
+- Maximum article words: 15,000
+- Minimum abstract words: 50
+- Maximum abstract words: 500
+- Minimum length ratio (article/abstract): 5
+- Maximum length ratio (article/abstract): 100
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("yilmazzey/arxiv_summarization_20k_preprocessed")
+
+# Access the data
+print(dataset['train'][0])
+# Output: {'article': '...', 'abstract': '...'}
+```
+
+## Use Cases
+
+This dataset is suitable for:
+- Training abstract generation models
+- Fine-tuning language models for summarization
+- Research on long-form text summarization
+- Evaluating summarization metrics (ROUGE, BLEU, etc.)
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{arxiv_summarization_20k_preprocessed,
+  title={ArXiv Summarization Dataset - 20K Preprocessed},
+  author={Yilmaz, Zeynep},
+  year={2024},
+  url={https://huggingface.co/datasets/yilmazzey/arxiv_summarization_20k_preprocessed}
+}
+```
+