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--- |
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language: |
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- ar |
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license: apache-2.0 |
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task_categories: |
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- summarization |
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- text-generation |
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pretty_name: Financial Reports Extractive Summarization Evaluation Dataset |
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tags: |
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- finance |
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- summarization |
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- extractive |
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- evaluation |
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- benchmark |
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- arabic |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: prompt |
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dtype: string |
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- name: full_text |
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dtype: string |
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- name: answer |
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dtype: string |
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- name: report_type |
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dtype: string |
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- name: file_name |
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dtype: string |
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- name: split |
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dtype: string |
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- name: text_length |
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dtype: int64 |
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- name: summary_length |
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dtype: int64 |
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- name: compression_ratio |
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dtype: float64 |
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splits: |
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- name: test |
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num_bytes: 680151 |
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num_examples: 80 |
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download_size: 298205 |
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dataset_size: 680151 |
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--- |
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# Financial Reports Extractive Summarization Evaluation Dataset |
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Validation and test splits for evaluating models on Arabic financial reports extractive summarization. |
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## Dataset Structure |
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- **Format**: Simple prompt-answer pairs |
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- **Validation**: ~20 examples (10%) |
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- **Test**: ~20 examples (10%) |
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- **Language**: Arabic |
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- **Domain**: Financial reports and market news |
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## Fields |
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- `id`: Unique identifier |
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- `prompt`: The summarization prompt |
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- `full_text`: Complete financial report |
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- `answer`: Ground truth extractive summary |
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- `report_type`: Type of report |
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- `file_name`: Original file |
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- `split`: 'validation' or 'test' |
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- `text_length`: Full text length |
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- `summary_length`: Summary length |
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- `compression_ratio`: Compression percentage |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("SahmBenchmark/financial-reports-extractive-summarization_eval") |
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# Access splits |
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val_data = dataset['validation'] |
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test_data = dataset['test'] |
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# For evaluation |
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for example in test_data: |
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model_output = model.generate(example['prompt']) |
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ground_truth = example['answer'] |
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# Calculate ROUGE scores |
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rouge_score = calculate_rouge(model_output, ground_truth) |
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``` |
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## Evaluation Metrics |
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- ROUGE-1, ROUGE-2, ROUGE-L |
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- Compression ratio accuracy |
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- Extractive accuracy (sentences from original) |
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For training data, see: `SahmBenchmark/financial-reports-extractive-summarization_train` |
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