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

arXiv: https://arxiv.org/abs/2511.05508 · HTML: https://mlsanity.com/paper/2511.05508/

## 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.

## Builds on (most cited first, 18 of 18)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [GSum: A General Framework for Guided Neural Abstractive Summarization](https://mlsanity.com/paper/2010.08014/) (2020)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination](https://mlsanity.com/paper/2311.15548/) (2023)
- [Refocusing on Relevance: Personalization in NLG](https://mlsanity.com/paper/2109.05140/) (2021)
- [Are Generative AI Agents Effective Personalized Financial Advisors?](https://mlsanity.com/paper/2504.05862/) (2025)
- [From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization](https://mlsanity.com/paper/2410.13961/) (2024)
- [Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency](https://mlsanity.com/paper/2407.21443/) (2024)
- [Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization](https://mlsanity.com/paper/2410.14545/) (2024)
- [FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance](https://mlsanity.com/paper/2508.05201/) (2025)
- [Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports](https://mlsanity.com/paper/2404.06162/) (2024)
- [PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models](https://mlsanity.com/paper/2410.03905/) (2024)
- [Are Large Language Models In-Context Personalized Summarizers? Get an iCOPERNICUS Test Done!](https://mlsanity.com/paper/2410.00149/) (2024)
- [Multi-round, Chain-of-thought Post-editing for Unfaithful Summaries](https://mlsanity.com/paper/2501.11273/) (2025)

## 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.