SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels
This addresses the need for topic-specific datasets to enhance news similarity detection systems for users, but it is incremental as it builds on existing methods by providing new data.
The authors tackled the problem of detecting redundant information in news articles by creating SPICED, a novel dataset with seven topics and four complexity levels, which improved model training by forcing them to learn salient characteristics in narrow domains, though no specific performance numbers were provided.
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.