Web News Timeline Generation with Extended Task Prompting.
CoRR(2023)
摘要
The creation of news timeline is essential for a comprehensive and contextual
understanding of events as they unfold over time. This approach aids in
discerning patterns and trends that might be obscured when news is viewed in
isolation. By organizing news in a chronological sequence, it becomes easier to
track the development of stories, understand the interrelation of events, and
grasp the broader implications of news items. This is particularly helpful in
sectors like finance and insurance, where timely understanding of the event
development-ranging from extreme weather to political upheavals and health
crises-is indispensable for effective risk management. While traditional
natural language processing (NLP) techniques have had some success, they often
fail to capture the news with nuanced relevance that are readily apparent to
domain experts, hindering broader industry integration. The advance of Large
Language Models (LLMs) offers a renewed opportunity to tackle this challenge.
However, direct prompting LLMs for this task is often ineffective. Our study
investigates the application of an extended task prompting technique to assess
past news relevance. We demonstrate that enhancing conventional prompts with
additional tasks boosts their effectiveness on various news dataset, rendering
news timeline generation practical for professional use. This work has been
deployed as a publicly accessible browser extension which is adopted within our
network.
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