Giveme5W1H: A Universal System for Extracting Main Events from News Articles
It addresses the lack of universally applicable open-source methods for event extraction in news analysis, benefiting researchers and practitioners in tasks like summarization and clustering.
The paper tackles the problem of event extraction from news articles by presenting an improved version of Giveme5W1H, a system that uses syntactic and domain-specific rules to extract answers to 5W1H questions, achieving an overall precision of 0.73 and 0.82 for the first four W questions in an expert evaluation with 120 articles.
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.