5W1H Extraction With Large Language Models
CoRR(2024)
Abstract
The extraction of essential news elements through the 5W1H framework
(What, When, Where, Why, Who, and
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 What, Why, and 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
(CNN/DailyMail, XSum, NYT, 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.
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