{"ID":2850487,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05508","arxiv_id":"2511.05508","title":"Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Support","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.","short_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, ev...","url_abs":"https://arxiv.org/abs/2511.05508","url_pdf":"https://arxiv.org/pdf/2511.05508v2","authors":"[\"Tianyi Zhang\",\"Mu Chen\"]","published":"2025-10-24T05:55:05Z","proceeding":"q-fin.GN","tasks":"[\"q-fin.GN\",\"cs.AI\",\"cs.CE\"]","methods":"[\"Language Model\"]","has_code":false}