Giveme5W1H: A Universal System for Extracting Main Events from News Articles

TL;DR

This paper presents Giveme5W1H, an improved, open-source system that automatically extracts main event information from news articles by answering the 5W1H questions, aiding summarization and analysis.

Contribution

The paper introduces an enhanced, universal version of Giveme5W1H that uses syntactic and domain-specific rules for effective event extraction from diverse news articles.

Findings

Overall precision of 0.73 in event extraction

High accuracy (0.82) in answering the first four W questions

System is publicly available and applicable across various news analysis tasks

Abstract

Event extraction from news articles is a commonly required prerequisite for various tasks, such as article summarization, article clustering, and news aggregation. Due to the lack of universally applicable and publicly available methods tailored to news datasets, many researchers redundantly implement event extraction methods for their own projects. The journalistic 5W1H questions are capable of describing the main event of an article, i.e., by answering who did what, when, where, why, and how. We provide an in-depth description of an improved version of Giveme5W1H, a system that uses syntactic and domain-specific rules to automatically extract the relevant phrases from English news articles to provide answers to these 5W1H questions. Given the answers to these questions, the system determines an article's main event. In an expert evaluation with three assessors and 120 articles, we determined an overall precision of p=0.73, and p=0.82 for answering the first four W questions, which alone can sufficiently summarize the main event reported on in a news article. We recently made our system publicly available, and it remains the only universal open-source 5W1H extractor capable of being applied to a wide range of use cases in news analysis.