Automatically Generating Macro Research Reports from a Piece of News
Wenxin Hu, Xiaofeng Zhang, Gang Yang

TL;DR
This paper presents a deep learning system that automatically generates macroeconomic research reports from economic news, aiming to assist analysts by providing draft reports quickly.
Contribution
It introduces a novel two-component deep learning approach for long text generation, specifically tailored for macroeconomic report drafting from news data.
Findings
The system effectively generates macro reports from news data.
Evaluation shows promising results with subjective quality assessments.
A large news-to-report dataset was used for training and evaluation.
Abstract
Automatically generating macro research reports from economic news is an important yet challenging task. As we all know, it requires the macro analysts to write such reports within a short period of time after the important economic news are released. This motivates our work, i.e., using AI techniques to save manual cost. The goal of the proposed system is to generate macro research reports as the draft for macro analysts. Essentially, the core challenge is the long text generation issue. To address this issue, we propose a novel deep learning technique based approach which includes two components, i.e., outline generation and macro research report generation.For the model performance evaluation, we first crawl a large news-to-report dataset and then evaluate our approach on this dataset, and the generated reports are given for the subjective evaluation.
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Taxonomy
TopicsTopic Modeling · Biomedical Text Mining and Ontologies · Natural Language Processing Techniques
