  The digital landscape is rapidly evolving with an ever-increasing volume of
online news, emphasizing the need for swift and precise analysis of complex
events. We refer to the complex events composed of many news articles over an
extended period as Temporal Complex Event (TCE). This paper proposes a novel
approach using Large Language Models (LLMs) to systematically extract and
analyze the event chain within TCE, characterized by their key points and
timestamps. We establish a benchmark, named TCELongBench, to evaluate the
proficiency of LLMs in handling temporal dynamics and understanding extensive
text. This benchmark encompasses three distinct tasks - reading comprehension,
temporal sequencing, and future event forecasting. In the experiment, we
leverage retrieval-augmented generation (RAG) method and LLMs with long context
window to deal with lengthy news articles of TCE. Our findings indicate that
models with suitable retrievers exhibit comparable performance with those
utilizing long context window.
