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