Summarization of Investment Reports Using Pre-trained Model

TL;DR

This paper explores the use of transformer-based models to automate the summarization of investment reports, comparing extractive and abstractive methods to improve efficiency in fund management documentation.

Contribution

It introduces both extractive and abstractive summarization techniques using pre-trained transformers for investment report summarization, evaluating their effectiveness.

Findings

Transformer models effectively summarize investment reports.

Abstractive summarization provides more concise summaries.

Extractive methods are more accurate but less concise.

Abstract

In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In addition to preparing monthly reports on fund management, fund managers prepare management reports that summarize these monthly reports every six months or once a year. The preparation of fund reports is a labor-intensive and time-consuming task. Therefore, in this paper, we tackle investment summarization from monthly reports using transformer-based models. There are two main types of summarization methods: extractive summarization and abstractive summarization, and this study constructs both methods and examines which is more useful in summarizing investment reports.