  The extraction of essential news elements through the 5W1H framework
(\textit{What}, \textit{When}, \textit{Where}, \textit{Why}, \textit{Who}, and
\textit{How}) is critical for event extraction and text summarization. The
advent of Large language models (LLMs) such as ChatGPT presents an opportunity
to address language-related tasks through simple prompts without fine-tuning
models with much time. While ChatGPT has encountered challenges in processing
longer news texts and analyzing specific attributes in context, especially
answering questions about \textit{What}, \textit{Why}, and \textit{How}. The
effectiveness of extraction tasks is notably dependent on high-quality
human-annotated datasets. However, the absence of such datasets for the 5W1H
extraction increases the difficulty of fine-tuning strategies based on
open-source LLMs. To address these limitations, first, we annotate a
high-quality 5W1H dataset based on four typical news corpora
(\textit{CNN/DailyMail}, \textit{XSum}, \textit{NYT}, \textit{RA-MDS}); second,
we design several strategies from zero-shot/few-shot prompting to efficient
fine-tuning to conduct 5W1H aspects extraction from the original news
documents. The experimental results demonstrate that the performance of the
fine-tuned models on our labelled dataset is superior to the performance of
ChatGPT. Furthermore, we also explore the domain adaptation capability by
testing the source-domain (e.g. NYT) models on the target domain corpus (e.g.
CNN/DailyMail) for the task of 5W1H extraction.
