Text representation is a key aspect in determining the success of various text summarizing techniques.Summarization using pretrained transformer models has produced encouraging results.Yet the scope of applying these ...Text representation is a key aspect in determining the success of various text summarizing techniques.Summarization using pretrained transformer models has produced encouraging results.Yet the scope of applying these models in medical and drug discovery is not examined to a proper extent.To address this issue,this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical domain.This research also aims to enhance sentence representation.Exploring the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are limited.Hence,this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug and COVID,with a total capacity of 1,370 abstracts.A detailed experimentation using BART(Bidirectional Autoregressive Transformer),T5(Text-to-Text Transfer Transformer),LexRank,and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.展开更多
Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections.Large-sc...Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections.Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection.It is always challenging to retrieve a more accurate and fast retrieval from a large collection.Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate data object as single or multiple documents from the collection.Machine learning and knowledge-based techniques are the two query-based extractive text summarization techniques in Natural Language Processing(NLP)which can be used for precise retrieval and are considered to be the best option.NLP uses machine learning approaches for both supervised and unsupervised learning for calculating probabilistic features.The study aims to propose a hybrid approach for query-based extractive text summarization in the research study.Text-Rank Algorithm is used as a core algorithm for the flow of an implementation of the approach to gain the required goals.Query-based text summarization of multiple documents using a hybrid approach,combining the K-Means clustering technique with Latent Dirichlet Allocation(LDA)as topic modeling technique produces 0.288,0.631,and 0.328 for precision,recall,and F-score,respectively.The results show that the proposed hybrid approach performs better than the graph-based independent approach and the sentences and word frequency-based approach.展开更多
Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process...Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document.In this study,a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns.One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods.In this study,attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision,continuity and consistency.To investigate the efficiency of the proposed algorithm,the results of the study were evaluated with respect to the precision and recall criteria.The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.展开更多
Nowadays,people use online resources such as educational videos and courses.However,such videos and courses are mostly long and thus,summarizing them will be valuable.The video contents(visual,audio,and subtitles)coul...Nowadays,people use online resources such as educational videos and courses.However,such videos and courses are mostly long and thus,summarizing them will be valuable.The video contents(visual,audio,and subtitles)could be analyzed to generate textual summaries,i.e.,notes.Videos’subtitles contain significant information.Therefore,summarizing subtitles is effective to concentrate on the necessary details.Most of the existing studies used Term Frequency-Inverse Document Frequency(TF-IDF)and Latent Semantic Analysis(LSA)models to create lectures’summaries.This study takes another approach and applies LatentDirichlet Allocation(LDA),which proved its effectiveness in document summarization.Specifically,the proposed LDA summarization model follows three phases.The first phase aims to prepare the subtitle file for modelling by performing some preprocessing steps,such as removing stop words.In the second phase,the LDA model is trained on subtitles to generate the keywords list used to extract important sentences.Whereas in the third phase,a summary is generated based on the keywords list.The generated summaries by LDA were lengthy;thus,a length enhancement method has been proposed.For the evaluation,the authors developed manual summaries of the existing“EDUVSUM”educational videos dataset.The authors compared the generated summaries with the manual-generated outlines using two methods,(i)Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and(ii)human evaluation.The performance of LDA-based generated summaries outperforms the summaries generated by TF-IDF and LSA.Besides reducing the summaries’length,the proposed length enhancement method did improve the summaries’precision rates.Other domains,such as news videos,can apply the proposed method for video summarization.展开更多
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 the era of Big Data,we are faced with an inevitable and challenging problem of“overload information”.To alleviate this problem,it is important to use effective automatic text summarization techniques to obtain th...In the era of Big Data,we are faced with an inevitable and challenging problem of“overload information”.To alleviate this problem,it is important to use effective automatic text summarization techniques to obtain the key information quickly and efficiently from the huge amount of text.In this paper,we propose a hybrid method of extractive text summarization based on deep learning and graph ranking algorithms(ETSDG).In this method,a pre-trained deep learning model is designed to yield useful sentence embeddings.Given the association between sentences in raw documents,a traditional LexRank algorithm with fine-tuning is adopted fin ETSDG.In order to improve the performance of the extractive text summarization method,we further integrate the traditional LexRank algorithm with deep learning.Testing results on the data set DUC2004 show that ETSDG has better performance in ROUGE metrics compared with certain benchmark methods.展开更多
Unsupervised extractive summarization aims to pinpoint representative sentences from raw text without relying on labeled summary data,capturing the overall content.Numerous prevalent research methods predominantly pri...Unsupervised extractive summarization aims to pinpoint representative sentences from raw text without relying on labeled summary data,capturing the overall content.Numerous prevalent research methods predominantly prioritize the significance of sentences within a document,potentially overlooking the importance of varying keywords within a sentence.Moreover,many methods confine the summarization to information present only in the current document,potentially omitting crucial details essential for comprehensive document understanding.To tackle these challenges,this paper introduces WSDSum,an algorithm rooted in word weight fusion and dynamic document comparison.This algorithm employs two distinctword weight assessmentmethods to gauge the significance of words in a sentence and subsequently combines their assessment outcomes to more effectively evaluate word importance within a sentence.Furthermore,this paper suggests a dynamic document comparison approach to enhance the diversity of the generated summaries by creating positive examples from intra-document sentences and contrasting them with inter-document sentence counterexamples.This is achieved by leveraging a cosine annealing strategy to facilitate dynamic temperature comparisons with other documents.Experimental evaluations on three public datasets indicate that WSDSum outperforms traditional methods.展开更多
文摘Text representation is a key aspect in determining the success of various text summarizing techniques.Summarization using pretrained transformer models has produced encouraging results.Yet the scope of applying these models in medical and drug discovery is not examined to a proper extent.To address this issue,this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical domain.This research also aims to enhance sentence representation.Exploring the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are limited.Hence,this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug and COVID,with a total capacity of 1,370 abstracts.A detailed experimentation using BART(Bidirectional Autoregressive Transformer),T5(Text-to-Text Transfer Transformer),LexRank,and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.
文摘Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections.Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection.It is always challenging to retrieve a more accurate and fast retrieval from a large collection.Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate data object as single or multiple documents from the collection.Machine learning and knowledge-based techniques are the two query-based extractive text summarization techniques in Natural Language Processing(NLP)which can be used for precise retrieval and are considered to be the best option.NLP uses machine learning approaches for both supervised and unsupervised learning for calculating probabilistic features.The study aims to propose a hybrid approach for query-based extractive text summarization in the research study.Text-Rank Algorithm is used as a core algorithm for the flow of an implementation of the approach to gain the required goals.Query-based text summarization of multiple documents using a hybrid approach,combining the K-Means clustering technique with Latent Dirichlet Allocation(LDA)as topic modeling technique produces 0.288,0.631,and 0.328 for precision,recall,and F-score,respectively.The results show that the proposed hybrid approach performs better than the graph-based independent approach and the sentences and word frequency-based approach.
文摘Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document.In this study,a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns.One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods.In this study,attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision,continuity and consistency.To investigate the efficiency of the proposed algorithm,the results of the study were evaluated with respect to the precision and recall criteria.The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.
文摘Nowadays,people use online resources such as educational videos and courses.However,such videos and courses are mostly long and thus,summarizing them will be valuable.The video contents(visual,audio,and subtitles)could be analyzed to generate textual summaries,i.e.,notes.Videos’subtitles contain significant information.Therefore,summarizing subtitles is effective to concentrate on the necessary details.Most of the existing studies used Term Frequency-Inverse Document Frequency(TF-IDF)and Latent Semantic Analysis(LSA)models to create lectures’summaries.This study takes another approach and applies LatentDirichlet Allocation(LDA),which proved its effectiveness in document summarization.Specifically,the proposed LDA summarization model follows three phases.The first phase aims to prepare the subtitle file for modelling by performing some preprocessing steps,such as removing stop words.In the second phase,the LDA model is trained on subtitles to generate the keywords list used to extract important sentences.Whereas in the third phase,a summary is generated based on the keywords list.The generated summaries by LDA were lengthy;thus,a length enhancement method has been proposed.For the evaluation,the authors developed manual summaries of the existing“EDUVSUM”educational videos dataset.The authors compared the generated summaries with the manual-generated outlines using two methods,(i)Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and(ii)human evaluation.The performance of LDA-based generated summaries outperforms the summaries generated by TF-IDF and LSA.Besides reducing the summaries’length,the proposed length enhancement method did improve the summaries’precision rates.Other domains,such as news videos,can apply the proposed method for video summarization.
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.
文摘In the era of Big Data,we are faced with an inevitable and challenging problem of“overload information”.To alleviate this problem,it is important to use effective automatic text summarization techniques to obtain the key information quickly and efficiently from the huge amount of text.In this paper,we propose a hybrid method of extractive text summarization based on deep learning and graph ranking algorithms(ETSDG).In this method,a pre-trained deep learning model is designed to yield useful sentence embeddings.Given the association between sentences in raw documents,a traditional LexRank algorithm with fine-tuning is adopted fin ETSDG.In order to improve the performance of the extractive text summarization method,we further integrate the traditional LexRank algorithm with deep learning.Testing results on the data set DUC2004 show that ETSDG has better performance in ROUGE metrics compared with certain benchmark methods.
文摘Unsupervised extractive summarization aims to pinpoint representative sentences from raw text without relying on labeled summary data,capturing the overall content.Numerous prevalent research methods predominantly prioritize the significance of sentences within a document,potentially overlooking the importance of varying keywords within a sentence.Moreover,many methods confine the summarization to information present only in the current document,potentially omitting crucial details essential for comprehensive document understanding.To tackle these challenges,this paper introduces WSDSum,an algorithm rooted in word weight fusion and dynamic document comparison.This algorithm employs two distinctword weight assessmentmethods to gauge the significance of words in a sentence and subsequently combines their assessment outcomes to more effectively evaluate word importance within a sentence.Furthermore,this paper suggests a dynamic document comparison approach to enhance the diversity of the generated summaries by creating positive examples from intra-document sentences and contrasting them with inter-document sentence counterexamples.This is achieved by leveraging a cosine annealing strategy to facilitate dynamic temperature comparisons with other documents.Experimental evaluations on three public datasets indicate that WSDSum outperforms traditional methods.
