This study proposes DisSim-FinBERT, a novel framework that integrates Discourse Simplification (DisSim) with Aspect-Based Sentiment Analysis (ABSA) to enhance sentiment prediction in complex financial texts. By simplifying intricate documents such as Federal Open Market Committee (FOMC) minutes, DisSim improves the precision of aspect identification, resulting in sentiment predictions that align more closely with economic events. The model preserves the original informational content and captures the inherent volatility of financial language, offering a more nuanced and accurate interpretation of long-form financial communications. This approach provides a practical tool for policymakers and analysts aiming to extract actionable insights from central bank narratives and other detailed economic documents.

翻译：本研究提出了一种新颖的框架DisSim-FinBERT，该框架将篇章简化（Discourse Simplification, DisSim）与基于方面的情感分析（Aspect-Based Sentiment Analysis, ABSA）相结合，以提升复杂金融文本中的情感预测性能。通过简化如联邦公开市场委员会（FOMC）会议纪要等复杂文档，DisSim提高了方面识别的精确度，从而使情感预测结果与经济事件更为吻合。该模型保留了原始信息内容，并捕捉了金融语言固有的波动性，为长篇金融通讯提供了更细致、更准确的解读。此方法为政策制定者和分析师提供了一种实用工具，旨在从央行叙述及其他详细经济文件中提取可操作的见解。