Automatic Generation of Labor Cost Analysis Report Combining GAN and NLP Technology

Abstract

In recent years, companies have faced a large number of data processing and report generation needs in the field of labor cost analysis. Traditional labor cost analysis report generation methods have the disadvantages of long generation time and error-proneness, so it is necessary to automatically and intelligently generate accurate labor cost analysis reports. To solve this problem, this study combines the Generative Adversarial Network (GAN) and Natural Language Processing (NLP) technology to automatically generate a labor cost analysis report that meets the requirements through the collaborative work of deep learning models. First, this paper uses GAN to generate simulated labor cost data to ensure the diversity and realism of training data; then, the text generation model in NLP technology is used to generate a structured analysis report based on this data to ensure the professionalism and accuracy of the report content; finally, by evaluating and optimizing the generated results, the quality and generation efficiency of the report are further improved. In the experimental results, when the data volume increased to 1000, the BLEU (Bilingual Evaluation) score increased to 0.80 and the ROUGE (Recall-Oriented Summary Evaluation) score increased to 0.85. In addition, the Flesch-Kincaid readability index decreased from 65 to 51, and the manual score increased from 4.2 to 5.0. The method proposed in this paper that combines GAN and NLP technology can effectively improve the performance of labor cost analysis reports and has broad application prospects.