SPICED: News Similarity Detection Dataset with Multiple Topics and
  Complexity Levels
Elena Shushkevich Long Mai Manuel V. Loureiro Steven Derby Tri Kurniawan Wijaya
Nowadays, the use of intelligent systems to detect redundant information in
news articles has become especially prevalent with the proliferation of news
media outlets 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 new 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 distinct approaches for
generating news pairs, which are used in the creation of datasets specifically
designed for news similarity detection task. We benchmarked the created
datasets using MinHash, BERT, SBERT, and SimCSE models.