Proposing task to extract differences from time series financial documents

Abstract

In the natural language processing in the financial domain, many studies focus on the analysis of documents at a certain point in time, while various documents such as financial statements and financial results are published regularly.For investors with many stocks, it is not easy to read every detail of documents that exist at two points in time about each company continuously.It is also difficult to find out what has changed since the last time published.In this study, we propose a task to extract differences from two similar sentences in the two financial results written about the same company.We automatically extract similar parts from the two documents.For the extracted parts, we manually extract the differences between the two sentences.In addition, we conduct an evaluation experiment with a pre-trained language model.