Financial News Summarization: Can extractive methods still offer a true alternative to LLMs?
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918239821365248 |
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| author | Reche, Nicolas Linhares-Pontes, Elvys Torres-Moreno, Juan-Manuel |
| author_facet | Reche, Nicolas Linhares-Pontes, Elvys Torres-Moreno, Juan-Manuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Financial News Summarization: Can extractive methods still offer a true alternative to LLMs? Reche, Nicolas Linhares-Pontes, Elvys Torres-Moreno, Juan-Manuel Computational Engineering, Finance, and Science 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. |
| title | Financial News Summarization: Can extractive methods still offer a true alternative to LLMs? |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2512.08764 |