Disentangled Variational Topic Inference for Topic-Accurate Financial Report Generation (2022)

Abstract: Automatic generating financial report from a set of news is important but challenging. The financial reports is composed of key points of the news and corresponding inferring and reasoning from specialists in financial domain with professional knowledge. The challenges lie in the effective learning of the extra knowledge that is not well presented in the news, and the misalignment between topic of input news and output knowledge in target reports. In this work, we introduce a disentangled variational topic inference approach to learn two latent variables for news and report, respectively. We use a publicly available dataset to evaluate the proposed approach. The results demonstrate its effectiveness of enhancing the language informativeness and the topic accuracy of the generated financial reports.

## introduction automatically generating long financial reports from a set of macro news have been recently studied with the objective to assist analysts to perform the time-consuming reporting task. a macro news, as shown in fig. 1 , is one paragraph with multiple sentences describing a finance-domain event with supporting details. the corresponding financial report is a longer paragraph with key points of the news and extended analysis, such as inferring and reasoning, with the financial knowledge of analysts. in the literature, long text generation has been well studied in the domain of natural language generation processing @xcite . specially, generating long text from the short text with domain-specific settings is still challenging. the encoder-decoder architecture is commonly employed, where the input news is encoded by a recurrent neural network (rnn) and fed to another rnn model to generate the target report. some recent works @xcite replaced the encoder and decoder with the transformer-based model to learn the long dependency in both news and report text. however, these encoder-decoder models tend to produce generic sentences without the inherent uncertainty in the generated report. this uncertainty arises from the fact that financial reports are written by human specialists with different levels of expertise styles and professional knowledge. naturally, the reports are very diverse. probabilistic modeling is reported to be able to learn the uncertainty and diversity of the long texts @xcite . by learning the stochastic latent variables, the high-level information, such as specialist inference style, is expected to be modeled. ren et al @xcite proposed a variational autoencoder (vae) method to handle the uncertainty of both news and the report. the background knowledge are learned as the conditional latent variable. in addition, a vae-based hybrid approach is proposed in @xcite where the report outline is employed as latent variable for vae decoder. these approaches alleviated the challenges of long text generation. however, the topic of both news and reports are not explicitly learned, where the coherence and coverage of the generated reports are not guaranteed. recently, in the data-to-report generation domain, najdenkoska et al @xcite proposed a variational model with topic inference to enhance the topic alignment, where a set of latent variables of sentence-level topics are employed. nevertheless, the topic misalignment between input data and corresponding reports still exists and makes the model hard to be learned. to address the existing issues of topic modeling and alignment in a unify way, we propose a disentangled variational topic inference (devti) approach to generate financial reports by the probabilistic latent variable model. in particular, we learn two disentangled latent variables as the topics of input news and target reports, respectively. ## conclusion in this work, we propose a disentangled variational topic inference (devti) approach to enhance the topic-accurate financial report generation. two latent variables are learned for the topic of news and extra knowledge of reports. the experiments show the effectiveness of the proposed devti is able to generate descriptive report with correct topics.