With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text...With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.展开更多
In recent research,deep learning algorithms have presented effective representation learning models for natural languages.The deep learningbased models create better data representation than classical models.They are ...In recent research,deep learning algorithms have presented effective representation learning models for natural languages.The deep learningbased models create better data representation than classical models.They are capable of automated extraction of distributed representation of texts.In this research,we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module,and also addresses memory 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.展开更多
文摘With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.
基金This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R113),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In recent research,deep learning algorithms have presented effective representation learning models for natural languages.The deep learningbased models create better data representation than classical models.They are capable of automated extraction of distributed representation of texts.In this research,we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module,and also addresses memory 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.
