task_categories:summarization language:en license:cc-by-4.0 size_categories:n<1K region:us
---
license: cc-by-4.0
task_categories:
- summarization
language:
- en
pretty_name: Custom CNN/Daily Mail Summarization Dataset
size_categories:
- n<1K
---
# Dataset Card for Custom Text Dataset

## Dataset Name
Custom CNN/Daily Mail Summarization Dataset

## Overview
This dataset is a custom version of the CNN/Daily Mail dataset, designed for text summarization tasks. It contains news articles and their corresponding summaries.

## Composition
The dataset consists of two splits:
- Train: 1 custom example
- Test: 100 examples from the original CNN/Daily Mail dataset

Each example contains:
- 'sentence': The full text of a news article
- 'labels': The summary of the article

## Collection Process
The training data is a custom example created manually, while the test data is sampled from the CNN/Daily Mail dataset (version 3.0.0) available on Hugging Face.

## Preprocessing
No specific preprocessing was applied beyond the original CNN/Daily Mail dataset preprocessing.

## How to Use
```python
from datasets import load_from_disk

# Load the dataset
dataset = load_from_disk("./results/custom_dataset/")

# Access the data
train_data = dataset['train']
test_data = dataset['test']

# Example usage
print(train_data['sentence'])
print(train_data['labels'])
```

## Evaluation
This dataset is intended for text summarization tasks. Common evaluation metrics include ROUGE scores, which measure the overlap between generated summaries and reference summaries.

## Limitations
- The training set is extremely small (1 example), which may limit its usefulness for model training.
- The test set is a subset of the original CNN/Daily Mail dataset, which may not represent the full diversity of news articles.

## Ethical Considerations
- The dataset contains news articles, which may include sensitive or biased content.
- Users should be aware of potential copyright issues when using news content for model training or deployment.
- Care should be taken to avoid generating or propagating misleading or false information when using models trained on this dataset.