PIECE: Protagonist Identification and Event Chronology Extraction for Enhanced Timeline Summarization
Tz-Huan Hsu, Li-Hsuan Chin, Yen-Hao Huang, Yi-Shin Chen · 2024
Timeline summarization involves condensing events from news articles to illustrate the temporal development of a specific topic. Traditional methods often extract events based on the number of related reports but tend to overlook the movement of protagonists, the leading actors participating in events that shape the progression of the topic. This oversight can result in the extraction of sensationalized events unrelated to the topic's progression, distracting readers from tracking the topic's development. To address this limitation, we propose a novel strategy that identifies protagonists through dependency relations and tracks changes in the context surrounding them over time using a multi-faceted temporal graph. This temporal graph is a sequence of graphs that effectively captures information progression and shifts over time. Our approach aims to build a biographical timeline with accurate chronology by identifying and following the movement of protagonists. Our experiments demonstrate that our method, PIECE, outperforms previous approaches in date assignment for timeline summarization across different language datasets.