Generative AI Approach to Distributed Summarization of Financial Narratives

Neelesh K. Shukla, Raghu Katikeri, Msp Raja, Gowtham Sivam, Shlok Yadav, Amit Vaid, Shreenivas Prabhakararao

Abstract: This paper presents our submission to the Financial Narrative Summarization (FNS) task at the FNP-2023 workshop. The FNS task involves the generation of concise summaries, not exceeding 1000 words, for annual financial reports composed in English, Spanish, and Greek. In our prior work, presented in FNP-2022, we introduced DiMSum [1], a novel framework designed to automatically identify crucial narrative sections within financial reports and quantify their weighted contributions. The field of Generative AI and Large Language Models (LLMs) has recently witnessed significant advancements, prompting us to explore their utility in summarizing financial reports. Our investigation revealed that LLMs, when left to their own devices often struggle to effectively summarize complex financial documents, necessitating external guidance. In this study, we demonstrate how LLMs, when guided by the DiMSum framework, exhibit substantial improvements in the quality of financial report summarization. To the best of our knowledge, this research marks the first instance of applying LLMs to the FNS task, offering a novel approach to enhancing the summarization of financial reports.

External IDs:dblp:conf/bigdataconf/ShuklaKRSYVP23a