A hybrid deep generative neural model for financial report generation

Generating long macro reports from a piece of breaking news is quite a challenging task. Essentially, this task is a long text generation problem from short text. Apparently, the difficulty of this task lies in the logic inference of human beings. To address this issue, this paper proposes a novel hybrid deep generative neural model which first learns the outline of the input news and then generates macro financial reports from the learnt outline. In the outline generation component, we generate the outline text using the framework of Pointer-Generator network with attention mechanism. In the target report generation component, we generate the macro financial reports by the revised VAE model. To train our end-to-end model, we have collected the experimental dataset containing over one hundred thousand pairs of news-report data. Extensive experiments are then evaluated on this dataset. The proposed model achieves the SOTA performance against both the baseline models and the state-of-the-art models with respect to evaluation criteria BLEU, ROUGE and human scores. Although the readability of the generated reports by our approach is better than that of the rest models, it remains an open problem which needs further efforts in the future.