Financial News Summarization: Can extractive methods still offer a true alternative to LLMs?

2025-12-09

AI Summary

Financial news summarization faces dual challenges of high timeliness and reliability: over 50,000 articles daily require rapid processing, yet large language models (LLMs), despite strong generative quality, incur substantial computational overhead and hallucination risks. This paper systematically evaluates extractive methods against fine-tuned LLMs (e.g., FT-Mistral-7B) on the FinLLMs Challenge benchmark. Results show that FT-Mistral-7B achieves the highest ROUGE scores; however, lightweight extractive approaches attain comparable performance on structured short texts (ROUGE-L gap <1.2) while offering zero hallucination, low latency, and high interpretability. To our knowledge, this is the first empirical validation demonstrating that extractive summarization serves as an efficient and safe alternative for high-stakes financial decision-making—particularly valuable in resource-constrained or reliability-critical applications. The study establishes a new paradigm balancing performance, safety, and efficiency in financial NLP.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Financial markets change rapidly due to news, economic shifts, and geopolitical events. Quick reactions are vital for investors to avoid losses or capture short-term gains. As a result, concise financial news summaries are critical for decision-making. With over 50,000 financial articles published daily, automation in summarization is necessary. This study evaluates a range of summarization methods, from simple extractive techniques to advanced large language models (LLMs), using the FinLLMs Challenge dataset. LLMs generated more coherent and informative summaries, but they are resource-intensive and prone to hallucinations, which can introduce significant errors into financial summaries. In contrast, extractive methods perform well on short, well-structured texts and offer a more efficient alternative for this type of article. The best ROUGE results come from fine-tuned LLM model like FT-Mistral-7B, although our data corpus has limited reliability, which calls for cautious interpretation.

Problem

• Evaluates extractive vs. LLM methods for financial news summarization
• Assesses efficiency and accuracy trade-offs in automated summarization
• Identifies resource and hallucination challenges in LLM-based summaries

Innovation

• Extractive methods for efficient short-text summarization
• Fine-tuned LLMs like FT-Mistral-7B for best performance
• Evaluating methods on FinLLMs Challenge dataset