@inproceedings{shushkevich-etal-2024-spiced,
    title = "{SPICED}: News Similarity Detection Dataset with Multiple Topics and Complexity Levels",
    author = "Shushkevich, Elena  and
      Mai, Long Thanh  and
      Loureiro, Manuel V.  and
      Derby, Steven  and
      Wijaya, Tri Kurniawan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1320",
    pages = "15181--15190",
    abstract = "The proliferation of news media outlets has increased the demand for intelligent systems capable of detecting redundant information in news articles in order to enhance user experience. However, the heterogeneous nature of news can lead to spurious findings in these systems: Simple heuristics such as whether a pair of news are both about politics can provide strong but deceptive downstream performance. Segmenting news similarity datasets into topics improves the training of these models by forcing them to learn how to distinguish salient characteristics under more narrow domains. However, this requires the existence of topic-specific datasets, which are currently lacking. In this article, we propose a novel dataset of similar news, SPICED, which includes seven topics: Crime {\&} Law, Culture {\&} Entertainment, Disasters {\&} Accidents, Economy {\&} Business, Politics {\&} Conflicts, Science {\&} Technology, and Sports. Futhermore, we present four different levels of complexity, specifically designed for news similarity detection task. We benchmarked the created datasets using MinHash, BERT, SBERT, and SimCSE models.",
}

The proliferation of news media outlets has increased the demand for intelligent systems capable of detecting redundant information in news articles in order to enhance user experience. However, the heterogeneous nature of news can lead to spurious findings in these systems: Simple heuristics such as whether a pair of news are both about politics can provide strong but deceptive downstream performance. Segmenting news similarity datasets into topics improves the training of these models by forcing them to learn how to distinguish salient characteristics under more narrow domains. However, this requires the existence of topic-specific datasets, which are currently lacking. In this article, we propose a novel dataset of similar news, SPICED, which includes seven topics: Crime {\&} Law, Culture {\&} Entertainment, Disasters {\&} Accidents, Economy {\&} Business, Politics {\&} Conflicts, Science {\&} Technology, and Sports. Futhermore, we present four different levels of complexity, specifically designed for news similarity detection task. We benchmarked the created datasets using MinHash, BERT, SBERT, and SimCSE models.
Shushkevich, Elena Mai, Long Thanh Loureiro, Manuel V. Derby, Steven Wijaya, Tri Kurniawan
SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels