Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Support

Paper: arXiv 2511.05508

Authors: Tianyi Zhang, Mu Chen

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 5.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a practical natural language processing framework for financial news summarization using Chain-of-Thought prompting and personalized keyword filtering. Math complexity is very low, relying mostly on standard NLP metrics like BLEU and ROUGE, but the empirical evaluation shows promising initial improvements over base GPT-4o outputs.

Research Flowchart

  flowchart TD
    A[Goal: Reduce financial news overload & generate actionable investment insights] --> B[Inputs: Raw Financial News & User-Specified Keywords]
    B --> C[Method: Personalized Chain-of-Thought Summarization Framework]
    C --> D[Process: Event-Driven Noise Reduction & Context Filtering]
    D --> E[Process: LLM-Generated Investor Narratives]
    E --> F[Outcome: Concise, Personalized & Actionable Multi-Asset Summaries]