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

arXiv 2511.05508 ↗First posted 24 Oct 2025q-fin.GN cs.AI cs.CEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 24 Oct 2025.

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18 of 42 references.

References · 18

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023Mistral 7Bcited by 3,164
  3. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
  4. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  5. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
  6. 2020GSum: A General Framework for Guided Neural Abstractive Summarizationcited by 129
  7. 2020CTRLsum: Towards Generic Controllable Text Summarizationcited by 98
  8. 2023Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucinationcited by 41
  9. 2021Refocusing on Relevance: Personalization in NLGcited by 18
  10. 2025Are Generative AI Agents Effective Personalized Financial Advisors?cited by 18
  11. 2024From Single to Multi: How LLMs Hallucinate in Multi-Document Summarizationcited by 17
  12. 2024Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistencycited by 14
6 more
  1. 2024Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarizationcited by 9
  2. 2025FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Financecited by 8
  3. 2024Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reportscited by 5
  4. 2024PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Modelscited by 5
  5. 2024Are Large Language Models In-Context Personalized Summarizers? Get an iCOPERNICUS Test Done!cited by 2
  6. 2025Multi-round, Chain-of-thought Post-editing for Unfaithful Summariescited by 1

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  1. 2026Automated Summarization of Financial News Using Large Language Models and Retrieval-Augmented Generation: An Early Empirical Study (Fall 2023)
  2. 2024FANAL -- Financial Activity News Alerting Language Modeling Framework
  3. 2025Analogy-Driven Financial Chain-of-Thought (AD-FCoT): A Prompting Approach for Financial Sentiment Analysis
  4. 2025FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph
  5. 2026CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News
  6. 2025Unlocking Financial Insights: An advanced Multimodal Summarization with Multimodal Output Framework for Financial Advisory Videos
  7. 2026Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling
  8. 2024FinBen: A Holistic Financial Benchmark for Large Language Models