Summarization of financial reports with TIBER

Machine Learning with Applications

Sophie Krimberg¹  Marina Litvak²  Natalia Vanetik³ 

[1] Software Engineering Department, Shamoon College of Engineering, Bialik 56, Beer Sheva, Israel;Corresponding author.;Software Engineering Department, Shamoon College of Engineering, Bialik 56, Beer Sheva, Israel;

关键词: Extractive summarization;    Financial reports;    Node embeddings;    BERT;   

DOI  :  

来源: DOAJ

【 摘 要 】

This paper reports an approach for summarizing financial texts that combine several techniques for sentence representation and neural document modeling. Our approach is extractive and it follows the classic pipeline of ranking and consequent selecting of the top-ranked text chunks. We evaluate our method on the financial reports provided in the Financial Narrative Summarization (FNS 2021) shared task. The data for the shared task was created and collected from publicly available UK annual reports published by firms listed on the London Stock Exchange. The reports composed FNS 2021 dataset are very long, have many sections, and are written in “financial” language using various special terms, numerical data, and tables. The results show that our approach outperforms the FNS topline with a very serious advantage. In addition to its performance, our approach is also time-efficient.

【 授权许可】

Unknown