The New Model Text Abstract Extraction with Machine Learning

In scientific research, the identification process of finding various relevant methods is carried out through in-depth analysis of various literatures. This analysis aims to evaluate the contribution of previous research novelties and extract valuable information from existing literature. This study focuses on the development of an abstract text extraction model from scientific literature using a machine learning model. The scientific literature abstract text dataset used in this study was obtained from the Garuda National Aggregator. The extraction models used include Conditional Random Field (CRF), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) to read and extract abstract text from scientific literature, as well as identify important entities in unstructured text. The results of the study show that the Bi-LSTM model is able to provide good performance in extracting entities from abstract text with Precision, Recall, and F1-Score values with a score of 0.92 which indicates its ability to recognize and classify entities with high accuracy and extract information in unstructured abstract text.