What type of study is this?

This is a Quantitative Study study.

November 1, 2025Open Access

Automated Financial Report Summarization Using Python: A PDF-Based Approach

Key Points

The automated summarization system efficiently generates summaries from complex financial reports, streamlining analyst tasks.
Natural Language Processing techniques enhance the summarization process for clearer insights and better decision-making.
Evaluation demonstrated significant improvements in ROUGE scores, showcasing strong performance against traditional models.
The automated approach aims to reduce workload for analysts, ultimately speeding up financial decision-making processes.

Abstract

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

Ask AI

Helpful

Bookmark

View Full Paper