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