  Embedding news articles is a crucial tool for multiple fields, such as media
bias detection, identifying fake news, and making news recommendations.
However, existing news embedding methods are not optimized to capture the
latent context of news events. Most embedding methods rely on full-text
information and neglect time-relevant embedding generation. In this paper, we
propose a novel lightweight method that optimizes news embedding generation by
focusing on entities and themes mentioned in articles and their historical
connections to specific events. We suggest a method composed of three stages.
First, we process and extract events, entities, and themes from the given news
articles. Second, we generate periodic time embeddings for themes and entities
by training time-separated GloVe models on current and historical data. Lastly,
we concatenate the news embeddings generated by two distinct approaches: Smooth
Inverse Frequency (SIF) for article-level vectors and Siamese Neural Networks
for embeddings with nuanced event-related information. We leveraged over
850,000 news articles and 1,000,000 events from the GDELT project to test and
evaluate our method. We conducted a comparative analysis of different news
embedding generation methods for validation. Our experiments demonstrate that
our approach can both improve and outperform state-of-the-art methods on shared
event detection tasks.
