SweDN

The SweDN 1.0 dataset is a valuable resource for natural language processing (NLP) tasks, specifically text summarization. Let's delve into the details:

1. Title and Subtitle:
2. Title: SweDN 1.0

3. Subtitle: A Swedish text summarization corpus

4. Description:

5. The SweDN 1.0 corpus is based on 1,963,576 news articles from the Swedish newspaper Dagens Nyheter (DN) spanning the years 2000 to 2020.

6. These articles have been filtered to resemble the CNN/DailyMail dataset in terms of their textual structure.

7. Purpose and Usage:

8. Model Development: SweDN 1.0 serves as a training resource for both extractive and abstractive text summarizers.
9. Intended Task: Given a text (article), the goal is to provide its summary.

10. Evaluation Measures: The recommended evaluation metrics include the harmonic mean of Bleu and Rouge, along with Rouge, BERTScore, and Coh-Metrix.

11. Data Details:

12. Language: Swedish
13. Number of Articles: The dataset comprises 38,121 news articles along with their corresponding preambles.
14. Format: The data is available in JSONL and TSV files, containing fields such as ID, headline, summary, article, and article category.

15. An additional file provides various statistics for each entry, including length measures, embedding similarity, and article category.

16. Ethical Considerations:

17. The dataset does not involve any specific data labeling or annotator characteristics.

18. As with any NLP dataset, it's essential to consider ethical aspects and potential biases.

19. References:

20. Monsen, J., & Jönsson, A. (2021). A method for building non-English corpora for abstractive text summarization. Proceedings of the CLARIN Annual Conference ¹.

(1) SweDN 1.0 | Språkbanken Text - Göteborgs universitet. https://spraakbanken.gu.se/en/resources/swedn. (2) Language resources | Språkbanken Text - Göteborgs universitet. https://spraakbanken.gu.se/en/resources/train. (3) Kylberg Texture Dataset v. 1.0 – Kylberg.org. https://kylberg.org/kylberg-texture-dataset-v-1-0/. (4) undefined. https://spraakbanken.gu.se/resurser/superlim.