A Novel Method for News Article Event-Based Embedding

TL;DR

This paper introduces a lightweight, event-focused news embedding method that leverages entity and theme extraction, historical data, and dual embedding techniques to improve event detection accuracy.

Contribution

The proposed approach uniquely combines time-aware embeddings and dual methods to enhance news article representation for event detection tasks.

Findings

Outperforms existing methods on shared event detection tasks

Utilizes over 850,000 news articles and 1 million events for evaluation

Incorporates time-separated GloVe models for dynamic embedding generation

Abstract

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.