Automated Financial Report Summarization Using Python: A PDF-Based Approach - Nugraha, Fahmi Rizky (Universitas Utpadaka Swastika, 2025)
Financial reports are often lengthy, complex, and filled with domain-specific jargon, making itdifficult for analysts and stakeholders to extract key insights efficiently. This study proposes anautomated summarization system using Natural Language Processing (NLP) techniques to generateconcise and coherent summaries of financial reports. The system employs a two-stage summarizationarchitecture combining extractive and abstractive methods based on Transformer models such asBART, PEGASUS, and T5. Evaluation on simulated financial document datasets demonstrates thatthe hybrid two-stage model achieves the highest ROUGE scores and information retention ratescompared to single-model baselines. The results indicate that NLP-driven summarization cansignificantly reduce analysts’ workload and improve financial decision-making speed