Prefix tuning with prompt augmentation for efficient financial news summarization

In financial markets, the sentiment expressed in news articles plays a pivotal role in interpreting and forecasting market trends, which also holds true for the task of financial news summarization (FNS). Leveraging AI models to analyze social science data, this paper employs financial sentiment to improve FNS effectiveness by introducing a novel method that combines the sentiment polarity extracted from financial news with prompt augmentation techniques to ensure that the generated summaries are emotionally consistent with the source articles. Specifically, the detected sentiments are embedded into prompts and provide directive instructions to the model to generate summaries. Furthermore, to address the problem of limited large-scale datasets for FNS and ensure more tailored results, we employed prefix tuning as a fine-tuning strategy. Preliminary results indicate that our combined methodology outperforms approaches that use only prefix tuning. The experimental findings further validate the significance of sentiment analysis in FNS, which enhances the accuracy of capturing and reflecting market sentiment, thereby yielding valuable insights into financial markets. This method not only improves the accuracy and relevance of summaries but also ensures that their content is emotionally consistent with the source news, offering a new perspective on financial news summarization.