Extraction of Important Sentences in Financial Documents Based on Business Confidence Information Using LSTM with Self-Attention Mechanism

Abstract

Investment trust and fund management companies have accumulated a large number of visit records that were summarized by their analysts after conducting hearings against companies. Such visit reports include crucial information of companies such as companies' financial conditions and future strategies, which are used to estimate investment values of individual companies. However, it is not easy even for skilled fund managers to derive suitable market outlooks and investment decisions from a huge amount of accumulated documents. In this research, to support investment decisions, we propose a new LSTM model with self-attention mechanism that can extract important sentences in analyst visit reports. Such extraction is conducted based on the sentence scoring, which is obtained as the weights in a self-attention mechanism. In our experiments for a set of 1,390 visit reports, we demonstrate that the proposed model has about 79% accuracy for extraction on average under the 5-fold cross-validation.