Poster presentation @ FNS 2020


The summarization architecture proposed for the FNS 2020 shared task is based on a three-phases process.

  • Preprocessing step: clean input financial reports and annotate its content at sentence level.
  • Training step: deep learning models are fine-tuned for the regression task exploiting the annotations obtained during the preprocessing step.
  • Evaluation phase: is applied at document level. The sentences of each annual reports make a forward pass through the fine-tuned model to obtain the estimated relevance score. The final summary merges sentences according to the relevance score predicted by the fine-tuned architecture.

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).