---
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`