Abstract
In recent research, deep learning algorithms have presented effective representation learning models for natural languages. The deep learning-based models create better data representation than classicalmodels. They are capable of automated extraction of distributed representation of texts. In this research, we introduce a newtree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module, and also addressesmemory issues that were not addresses before. The proposed model employs a tree structured mechanism to generate the phrase and text embedding. The proposed architecture mimics the tree configuration of the text-texts and provide better feature representation. It also incorporates an attention mechanism that offers an additional information source to conduct better summary extraction. The novel model addresses text summarization as a classification process, where the model calculates the probabilities of phrase and text-summary association. The model classification is divided into multiple features recognition such as information entropy, significance, redundancy and position. The model was assessed on two datasets, on the Multi-Doc Composition Query (MCQ) and Dual Attention Composition dataset (DAC) dataset. The experimental results prove that our proposed model has better summarization precision vs. other models by a considerable margin.