学習データの自動生成による深層学習を用いた株主招集通知の重要ページ抽出

抄録

A shareholder convocation notice is a letter that the company is obliged to send to shareholders when holding a shareholder’s meeting. We can access it on corporate websites and acquire as PDF file. It contains a lot of useful information, such as company profile, major shareholders, and bills to be discussed. Therefore, institutional investors often use that information in their investment decisions. However, the following challenges exist for institutional investors to extract information that is likely to affect the stock price. The number of pages ranges from more than a dozen to more than a hundred. In addition, since they are issued before a shareholder’s meetings, they are issued in large numbers in a particular month, i.e., thousands of company notices are issued in June, the most concentrated month. This is a significant burden for institutional investors.

The purpose of our research is to automatically extract pages that are likely to affect the stock price from shareholder convocation notices. To this end, we need to tag the pages to automatically extract what information is described on a page-by-page basis. In our research, we propose the following framework: We automatically create training data by a rule-based method and train the deep learning model that extracts important pages. We confirm the effectiveness of our framework for pages that cannot be extracted by the rule-based method.