The rapid growth of textual data published everyday on the web has prompted the need for automatic text summarization methodologies. In the financial domain, quoted companies are required to periodically publish a report in textual form. Depending on the reader, those reports may contain redundant or irrelevant information. The proposed task aims at highlighting business-relevant information while analyzing the annual reports. The proposed methodology exploit the advancements in the Natural Language Understanding field to create a fine-tuned architecture able to specialize the general knowledge towards the financial domain.
Recommended citation: La Quatra, M., & Cagliero, L. (2020, December). End-to-end Training For Financial Report Summarization. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (pp. 118-123).