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.
Topic models aim to reveal the latent structure behind a corpus, typically conducted over a bag-of-words representation of documents. In the context of topic modeling, most vocabulary is either irrelevant for uncovering underlying topics or contains strong relationships with relevant concepts, impacting the interpretability of these topics. Furthermore, their limited expressiveness and dependency on language demand considerable computation resources. Hence, we propose a novel approach for cluster-based topic modeling that employs conceptual entities. Entities are language-agnostic representations of real-world concepts rich in relational information. To this end, we extract vector representations of entities from (i) an encyclopedic corpus using a language model; and (ii) a knowledge base using a graph neural network. We demonstrate that our approach consistently outperforms other state-of-the-art topic models across coherency metrics and find that the explicit knowledge encoded in the graph-based embeddings provides more coherent topics than the implicit knowledge encoded with the contextualized embeddings of language models.