Advanced NLP Techniques for Summarizing Multilingual Financial Narratives from Global Annual Reports

Dilshod Azizov, Jiyong Li, Hilal AlQuabeh, Shangsong Liang

Abstract: The increasing volume of financial documents requires efficient summarization methods. This study investigates the use of natural language processing (NLP) to summarize financial narratives from annual reports in English, Spanish, and Greek. We employ T5 for English and mT5 for Greek and Spanish, generating structured summaries of firms’ yearly financial trends. Despite the challenges posed by diverse and unstructured reports, our approach effectively identifies key narrative elements, excluding financial tables and numerical data. In competition, our system significantly exceeded the baseline model, with placements varying by language and a weighted score distribution of 50% for English, 25% for Greek and 25% for Spanish, producing a composite score of 0.112.