Parte de processamento de lingua natural do trabalho (geração de relatórios de produção energética):
Regarding report generation (text generation models), a vast number of works dive in the topic of generating medical reports, wether it is to automated patient-related diagnosis reports, medical image reports, radiology reports, etc. [1,2,3,4], majority of which use Recursive Neural Networks (RNN) to achieve so. In the energy field, the works focus more in forecasting energy production and consumption itself (as mentioned in the previous paragraphs), but when it comes to report generation based on either energy production or consumption forecast, no studies in the lierature have been found so far. However, many studies gravitate around text generation using Natural Language Processing (NLP) models wether it is to simply build models to generate text or to build pre-trained models capable of being trained towards specific scennarios, as shown repectively in the surveys [5,6].
Parte de processamento de lingua natural do trabalho (geração de relatórios de produção energética):
Regarding report generation (text generation models), a vast number of works dive in the topic of generating medical reports, wether it is to automated patient-related diagnosis reports, medical image reports, radiology reports, etc. [1,2,3,4], majority of which use Recursive Neural Networks (RNN) to achieve so. In the energy field, the works focus more in forecasting energy production and consumption itself (as mentioned in the previous paragraphs), but when it comes to report generation based on either energy production or consumption forecast, no studies in the lierature have been found so far. However, many studies gravitate around text generation using Natural Language Processing (NLP) models wether it is to simply build models to generate text or to build pre-trained models capable of being trained towards specific scennarios, as shown repectively in the surveys [5,6].
[1] - https://doi.org/10.1136/jamia.1999.0060076 [2] - https://doi.org/10.1016/j.artmed.2020.101878 [3] - 10.1109/ICDM.2019.00083 [4] - 10.1109/JBHI.2020.2980118 [5] - https://doi.org/10.1016/j.jksuci.2020.04.001 - The survey: Text generation models in deep learning [6] - https://doi.org/10.48550/arXiv.2105.10311 - Pretrained Language Models for Text Generation: A Survey