Datasets:
Tasks:
Summarization
Sub-tasks:
news-articles-summarization
Languages:
English
Size Categories:
10K<n<100K
Source Datasets:
original
Tags:
License:
| language: | |
| - en | |
| license: | |
| - cc-by-nc-4.0 | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - summarization | |
| task_ids: | |
| - news-articles-summarization | |
| dataset_info: | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: prediction | |
| list: | |
| - name: score | |
| dtype: float64 | |
| - name: text | |
| dtype: string | |
| - name: prediction_agent | |
| dtype: string | |
| - name: annotation | |
| dtype: 'null' | |
| - name: annotation_agent | |
| dtype: 'null' | |
| - name: id | |
| dtype: string | |
| - name: metadata | |
| dtype: 'null' | |
| - name: status | |
| dtype: string | |
| - name: event_timestamp | |
| dtype: timestamp[us] | |
| - name: metrics | |
| struct: | |
| - name: text_length | |
| dtype: int64 | |
| splits: | |
| - name: train | |
| num_bytes: 2563132.0446374374 | |
| num_examples: 1000 | |
| - name: test | |
| num_bytes: 52331466.955362566 | |
| num_examples: 20417 | |
| download_size: 33207109 | |
| dataset_size: 54894599.0 | |
| # Dataset Card for "news-summary" | |
| ## Dataset Description | |
| - **Homepage:** Kaggle Challenge | |
| - **Repository:** https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset?select=True.csv | |
| - **Paper:** N.A. | |
| - **Leaderboard:** N.A. | |
| - **Point of Contact:** N.A. | |
| ### Dataset Summary | |
| Officially it was supposed to be used for classification but, can you use this data set to summarize news articles? | |
| ### Languages | |
| english | |
| ### Citation Information | |
| Acknowledgements | |
| Ahmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018. | |
| Ahmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138). | |
| ### Contributions | |
| Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset. |