rahular/varta-urls

Dataset Summary

Varta is a diverse, challenging, large-scale, multilingual, and high-quality headline-generation dataset containing 41.8 million news articles in 14 Indic languages and English. The data is crawled from DailyHunt, a popular news aggregator in India that pulls high-quality articles from multiple trusted and reputed news publishers.

Languages

Assamese, Bhojpuri, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Tamil, Telugu, and Urdu.

Dataset Structure

Data Instances

{
"id":"n400000150",
"langCode":"as",
"source_url":"https://www.etvbharat.com/assamese/assam/bharat/militant-hideout-destroyed-on-srinagar-bandipora-highway/assam20220630074145729729173",
"dh_url":"https://m.dailyhunt.in/news/india/assamese/etvbharatassamese-epaper-dh6b381d65c3344bbcad9a06ee28b4ab2a/boma+nikshepeve+dhbans+kva+hl+santvasabadiv+aatmagopanasthali-newsid-n400000150"
}

Data Fields

• id: unique identifier for the artilce on DailyHunt. This id will be used to recreate the dataset.
• langCode: ISO 639-1 language code
• source_url: the url that points to the article on the website of the original publisher
• dh_url: the url that points to the article on DailyHunt

Data Splits

From every language, we randomly sample 10,000 articles each for validation and testing. We also ensure that at least 80% of a language’s data is available for training. Therefore, if a language has less than 100,000 articles, we restrict its validation and test splits to 10% of its size.

We also create a small training set by limiting the number of articles from each language to 100K. This small training set with a size of 1.3M is used in all our fine-tuning experiments. You can find the small training set here

Data Recreation

To recreate the dataset, follow this README file.

Misc

• Original source: https://m.dailyhunt.in/
• License: CC-BY 4.0

Citation Information

@misc{aralikatte2023varta,
      title={V\=arta: A Large-Scale Headline-Generation Dataset for Indic Languages}, 
      author={Rahul Aralikatte and Ziling Cheng and Sumanth Doddapaneni and Jackie Chi Kit Cheung},
      year={2023},
      eprint={2305.05858},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}