{"url":"/dataset/swedn","name":"SweDN","full_name":null,"description_markdown":"The **SweDN 1.0 dataset** is a valuable resource for natural language processing (NLP) tasks, specifically **text summarization**. Let's delve into the details:\r\n\r\n1. **Title and Subtitle**:\r\n   - **Title**: SweDN 1.0\r\n   - **Subtitle**: A Swedish text summarization corpus\r\n\r\n2. **Description**:\r\n   - 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**.\r\n   - These articles have been filtered to resemble the **CNN/DailyMail dataset** in terms of their textual structure.\r\n\r\n3. **Purpose and Usage**:\r\n   - **Model Development**: SweDN 1.0 serves as a training resource for both **extractive** and **abstractive** text summarizers.\r\n   - **Intended Task**: Given a text (article), the goal is to provide its summary.\r\n   - **Evaluation Measures**: The recommended evaluation metrics include the **harmonic mean of Bleu and Rouge**, along with **Rouge**, **BERTScore**, and **Coh-Metrix**.\r\n\r\n4. **Data Details**:\r\n   - **Language**: Swedish\r\n   - **Number of Articles**: The dataset comprises **38,121 news articles** along with their corresponding preambles.\r\n   - **Format**: The data is available in **JSONL** and **TSV** files, containing fields such as **ID**, **headline**, **summary**, **article**, and **article category**.\r\n   - An additional file provides various statistics for each entry, including **length measures**, **embedding similarity**, and **article category**.\r\n\r\n5. **Ethical Considerations**:\r\n   - The dataset does not involve any specific data labeling or annotator characteristics.\r\n   - As with any NLP dataset, it's essential to consider ethical aspects and potential biases.\r\n\r\n6. **References**:\r\n   - Monsen, J., & Jönsson, A. (2021). *A method for building non-English corpora for abstractive text summarization*. Proceedings of the CLARIN Annual Conference ¹.\r\n\r\n(1) SweDN 1.0 | Språkbanken Text - Göteborgs universitet. https://spraakbanken.gu.se/en/resources/swedn.\r\n(2) Language resources | Språkbanken Text - Göteborgs universitet. https://spraakbanken.gu.se/en/resources/train.\r\n(3) Kylberg Texture Dataset v. 1.0 – Kylberg.org. https://kylberg.org/kylberg-texture-dataset-v-1-0/.\r\n(4) undefined. https://spraakbanken.gu.se/resurser/superlim.","description_withheld":null,"homepage":"https://spraakbanken.gu.se/en/resources/swedn","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["SweDN"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}