Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Support
This addresses the challenge for financial advisors and investors in filtering noise from financial news to support timely investment decisions, though it appears incremental as it adapts existing CoT methods to a specific domain.
The paper tackles the problem of information overload in financial news by proposing a Chain-of-Thought summarization framework that generates personalized, event-driven summaries based on user keywords, resulting in concise outputs that highlight relevant contexts for investors.
Financial advisors and investors struggle with information overload from financial news, where irrelevant content and noise obscure key market signals and hinder timely investment decisions. To address this, we propose a novel Chain-of-Thought (CoT) summarization framework that condenses financial news into concise, event-driven summaries. The framework integrates user-specified keywords to generate personalized outputs, ensuring that only the most relevant contexts are highlighted. These personalized summaries provide an intermediate layer that supports language models in producing investor-focused narratives, bridging the gap between raw news and actionable insights.