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Automatic document classification via transformers for regulations compliance management in large utility companies

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Abstract

The operation of large utility companies such as Consolidated Edison Company of New York, Inc. (Con Edison) typically rely on large quantities of regulation documents from external institutions which inform the company of upcoming or ongoing policy changes or new requirements the company might need to comply with if deemed applicable. As a concrete example, if a recent regulatory publication mentions that the timeframe for the Company to respond to a reported system emergency in its service territory changes from within X time to within Y time—then the affected operating groups will be notified, and internal Company operating procedures may need to be reviewed and updated accordingly to comply with the new regulatory requirement. Each such regulation document needs to be reviewed manually by an expert to determine if the document is relevant to the company and, if so, which department it is relevant to. In order to help enterprises improve the efficiency of their operation, we propose an automatic document classification pipeline that determines whether a document is important for the company or not, and if deemed important it forwards those documents to the departments within the company for further review. Binary classification task of determining the importance of a document is done via ensembling the Naive Bayes (NB), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) together for the final prediction, whereas the multi-label classification problem of identifying the relevant departments for a document is executed by the transformer-based DocBERT model. We apply our pipeline to a large corpus of tens of thousands of text data provided by Con Edison and achieve an accuracy score over \(80\%\). Compared with existing solutions for document classification which rely on a single classifier, our paper i) ensemble multiple classifiers for better accuracy results and escaping from the problem of overfitting, ii) utilize pretrained transformer-based DocBERT model to achieve ideal performance for multi-label classification task and iii) introduce a bi-level structure to improve the performance of the whole pipeline where the binary classification module works as a rough filter before finally distributing the text to corresponding departments through the multi-label classification module.

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Data availability

The datasets analyzed during the current study are not publicly available due to commercial and privacy restriction since the datasets contain detailed regulation documents that are confidential to Consolidated Edison Company of New York Inc. (Con Edison) but are available from the corresponding author on reasonable request.

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  1. Tolga Dimlioglu and Jing Wang have equally contributed to this work.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, New York University, 5 Metro Tech, Brooklyn, NY, 11201, USA

    Tolga Dimlioglu, Jing Wang, Devansh Bisla & Anna Choromanska

  2. Research and Development Department, et al., Consolidated Edison Company of New York Inc. (Con Edison), 4 Irving Place, New York, NY, 10003, USA

    Simon Odie, Leon Bukhman, Afolabi Olomola & James D. Wong

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Appendices

Appendix A Detailed Pipeline Figure

In the figure below, the detailed diagram for the pipeline is provided. See Fig. 9.

Fig. 9
figure 9

Detailed diagram of the pipeline

Full size image

Appendix B Binary classifier comparison on different dataset

We provide the results for four soft classifiers not only with train and validation dataset accuracy (shown in Table 20), but also the held-out test dataset. Even though SVM perform better on the held-out test dataset, it could not outperforms other classifiers on the original train and validation dataset. Moreover, not a single soft classifier could beat the final ensembling strategy on the held-out test dataset provided in Section 5.1.3 with held-test accuracy \(92\%\).

Table 20 Training accuracy and final validation accuracy of ANN2, NB, SVM and RF
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Dimlioglu, T., Wang, J., Bisla, D. et al. Automatic document classification via transformers for regulations compliance management in large utility companies. Neural Comput & Applic 35, 17167–17185 (2023). https://doi.org/10.1007/s00521-023-08555-4

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