5W1H Extraction With Large Language Models

TL;DR

This paper introduces a high-quality 5W1H dataset for news articles and compares various prompting and fine-tuning strategies, demonstrating improved extraction performance over ChatGPT and exploring domain adaptation capabilities.

Contribution

The paper provides a new annotated dataset for 5W1H extraction and evaluates multiple strategies, including fine-tuning, for improved performance over existing LLMs.

Findings

Fine-tuned models outperform ChatGPT on 5W1H extraction.

High-quality annotated datasets enhance extraction accuracy.

Domain adaptation shows promising transferability across news corpora.

Abstract

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.