The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Curr...The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.展开更多
技术作为一种快速感知视频内容的方式得到了广泛的关注.现有基于图模型的视频摘要方法将视频帧作为顶点,通过边表示两个顶点之间的关系,但并不能很好地捕获视频帧之间的复杂关系.为了克服该缺点,本文提出了一种基于超图排序算法的静态...技术作为一种快速感知视频内容的方式得到了广泛的关注.现有基于图模型的视频摘要方法将视频帧作为顶点,通过边表示两个顶点之间的关系,但并不能很好地捕获视频帧之间的复杂关系.为了克服该缺点,本文提出了一种基于超图排序算法的静态视频摘要方法(Hyper-Graph Ranking based Video Summarization,HGRVS).HGRVS方法首先通过构建视频超图模型,将任意多个有内在关联的视频帧使用一条超边连接;然后提出一种基于超图排序的视频帧分类算法将视频帧按内容分类;最后通过求解提出的一种优化函数来生成静态视频摘要.在Open Video Project和YouTube两个数据集上的大量主观与客观实验验证了所提HGRVS算法的优良性能.展开更多
针对当前图摘要方法压缩率较高,图压缩算法无法直接被用于下游任务分析的问题,提出一种图摘要与图压缩的融合算法,即基于节点相似性分组与图压缩的图摘要算法(GSNSC)。首先,初始化节点为超节点,并根据相似度对超节点分组;其次,将每个组...针对当前图摘要方法压缩率较高,图压缩算法无法直接被用于下游任务分析的问题,提出一种图摘要与图压缩的融合算法,即基于节点相似性分组与图压缩的图摘要算法(GSNSC)。首先,初始化节点为超节点,并根据相似度对超节点分组;其次,将每个组的超节点合并,直到达到指定次数或指定节点数;再次,在超节点之间添加超边和校正边以恢复原始图;最后,对于图压缩部分,判断对每个超节点的邻接边压缩和摘要的代价,并选择二者中代价较小的执行。在Web-NotreDame、Web-Google和Web-Berkstan等6个数据集上进行了图压缩率和图查询实验。实验结果表明,在6个数据集上,与SLUGGER(Scalable Lossless sUmmarization of Graphs with HiERarchy)算法相比,所提算法的压缩率至少降低了23个百分点;与SWeG(Summarization of Web-scale Graphs)算法相比,所提算法的压缩率至少降低了13个百分点;在Web-NotreDame数据集上,所提算法的度误差比SWeG降低了41.6%。以上验证了所提算法具有更好的图压缩率和图查询准确度。展开更多
Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in r...Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and stability.Thus,this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First,the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then,an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.展开更多
Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.Ho...Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.However,there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics.In particular,Chinese complaint reports,generated by urban complainers and collected by government employees,describe existing resident problems in daily life.Meanwhile,the reflected problems are required to respond speedily.Therefore,automatic summarization tasks for these reports have been developed.However,similar to traditional summarization models,the generated summaries still exist problems of informativeness and conciseness.To address these issues and generate suitably informative and less redundant summaries,a topic-based abstractive summarization method is proposed to obtain global and local features.Additionally,a heterogeneous graph of the original document is constructed using word-level and topic-level features.Experiments and analyses on public review datasets(Yelp and Amazon)and our constructed dataset(Chinese complaint reports)show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.展开更多
Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort.Evaluating and selecting the most informati...Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort.Evaluating and selecting the most informative sentences from biomedical articles is always challenging.This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information.The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model.The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them.The proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial sentences.The quality of the framework is assessed via different parameters like information retention,coverage,readability,cohesion,and ROUGE scores in clustering and non-clustering modes.The significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory consumption.The configurable settings of combined parameters reduce execution time,enhance memory utilization,and extract relevant information outperforming other biomedical baseline models.An improvement of 17%is achieved when the proposed model is checked against similar biomedical text summarizers.展开更多
Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is p...Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is presented, that not only sentences relevance but also phrases relevance information included in sentences are utilized. In a word, we construct a phrase-sentence two-layer graph structure model （PSG） to summarize document（s） . We use this model for generic document summarization and query-focused sum- marization. The experimental results show that our model greatly outperforms existing work.展开更多
视频摘要以简短的内容概括原始视频,是对原始视频的浓缩或者总结。传统的视频摘要方法有基于聚类的方法,通过视频帧的局部或全局特征将视频帧分成多个有代表性的集群,在每个集群中提取关键帧,最后去除冗余生成摘要;也有基于图模型的方法...视频摘要以简短的内容概括原始视频,是对原始视频的浓缩或者总结。传统的视频摘要方法有基于聚类的方法,通过视频帧的局部或全局特征将视频帧分成多个有代表性的集群,在每个集群中提取关键帧,最后去除冗余生成摘要;也有基于图模型的方法,将视频帧作为顶点,连接顶点的边表示帧间的关系,通过图的模块化信息提取关键帧。文章将聚类方法和图模型的方法结合并优化,提出了一种新的基于最短路径算法的视频摘要方法(shortest path for video summary,SPVS)。SPVS方法将视频摘要问题转化为最短路径求解问题,路径上的帧即为关键帧,在数据集上的大量主客观实验结果都表明其具有良好的性能。展开更多
With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is increased.Here,document summarization that is extracting the essential task with appropriate inform...With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is increased.Here,document summarization that is extracting the essential task with appropriate information,removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task.In this research,a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation(gFAR).Initially,the graph model is used to map the relationship among the data(multi-source)followed by the establishment of document clustering with the generation of association rule using the fuzzy concept.This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy.This framework is provided in an interpretable way for document clustering.It iteratively reduces the error rate during relationship mapping among the data(clusters)with the assistance of weighted document content.Also,this model represents the significance of data features with class discrimination.It is also helpful in measuring the significance of the features during the data clustering process.The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns(RRP),ROUGE score,and Discrimination Information Measure(DMI)respectively.Here,DailyMail and DUC 2004 dataset is used to extract the empirical results.The proposed gFAR model gives better trade-off while compared with various prevailing approaches.展开更多
文摘The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.
文摘技术作为一种快速感知视频内容的方式得到了广泛的关注.现有基于图模型的视频摘要方法将视频帧作为顶点,通过边表示两个顶点之间的关系,但并不能很好地捕获视频帧之间的复杂关系.为了克服该缺点,本文提出了一种基于超图排序算法的静态视频摘要方法(Hyper-Graph Ranking based Video Summarization,HGRVS).HGRVS方法首先通过构建视频超图模型,将任意多个有内在关联的视频帧使用一条超边连接;然后提出一种基于超图排序的视频帧分类算法将视频帧按内容分类;最后通过求解提出的一种优化函数来生成静态视频摘要.在Open Video Project和YouTube两个数据集上的大量主观与客观实验验证了所提HGRVS算法的优良性能.
文摘针对当前图摘要方法压缩率较高,图压缩算法无法直接被用于下游任务分析的问题,提出一种图摘要与图压缩的融合算法,即基于节点相似性分组与图压缩的图摘要算法(GSNSC)。首先,初始化节点为超节点,并根据相似度对超节点分组;其次,将每个组的超节点合并,直到达到指定次数或指定节点数;再次,在超节点之间添加超边和校正边以恢复原始图;最后,对于图压缩部分,判断对每个超节点的邻接边压缩和摘要的代价,并选择二者中代价较小的执行。在Web-NotreDame、Web-Google和Web-Berkstan等6个数据集上进行了图压缩率和图查询实验。实验结果表明,在6个数据集上,与SLUGGER(Scalable Lossless sUmmarization of Graphs with HiERarchy)算法相比,所提算法的压缩率至少降低了23个百分点;与SWeG(Summarization of Web-scale Graphs)算法相比,所提算法的压缩率至少降低了13个百分点;在Web-NotreDame数据集上,所提算法的度误差比SWeG降低了41.6%。以上验证了所提算法具有更好的图压缩率和图查询准确度。
文摘Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and stability.Thus,this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First,the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then,an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.
基金supported byNationalNatural Science Foundation of China(52274205)and Project of Education Department of Liaoning Province(LJKZ0338).
文摘Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.However,there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics.In particular,Chinese complaint reports,generated by urban complainers and collected by government employees,describe existing resident problems in daily life.Meanwhile,the reflected problems are required to respond speedily.Therefore,automatic summarization tasks for these reports have been developed.However,similar to traditional summarization models,the generated summaries still exist problems of informativeness and conciseness.To address these issues and generate suitably informative and less redundant summaries,a topic-based abstractive summarization method is proposed to obtain global and local features.Additionally,a heterogeneous graph of the original document is constructed using word-level and topic-level features.Experiments and analyses on public review datasets(Yelp and Amazon)and our constructed dataset(Chinese complaint reports)show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.
文摘Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort.Evaluating and selecting the most informative sentences from biomedical articles is always challenging.This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information.The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model.The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them.The proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial sentences.The quality of the framework is assessed via different parameters like information retention,coverage,readability,cohesion,and ROUGE scores in clustering and non-clustering modes.The significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory consumption.The configurable settings of combined parameters reduce execution time,enhance memory utilization,and extract relevant information outperforming other biomedical baseline models.An improvement of 17%is achieved when the proposed model is checked against similar biomedical text summarizers.
文摘Graph model has been widely applied in docu- ment summarization by using sentence as the graph node, and the similarity between sentences as the edge. In this paper, a novel graph model for document summarization is presented, that not only sentences relevance but also phrases relevance information included in sentences are utilized. In a word, we construct a phrase-sentence two-layer graph structure model （PSG） to summarize document（s） . We use this model for generic document summarization and query-focused sum- marization. The experimental results show that our model greatly outperforms existing work.
文摘视频摘要以简短的内容概括原始视频,是对原始视频的浓缩或者总结。传统的视频摘要方法有基于聚类的方法,通过视频帧的局部或全局特征将视频帧分成多个有代表性的集群,在每个集群中提取关键帧,最后去除冗余生成摘要;也有基于图模型的方法,将视频帧作为顶点,连接顶点的边表示帧间的关系,通过图的模块化信息提取关键帧。文章将聚类方法和图模型的方法结合并优化,提出了一种新的基于最短路径算法的视频摘要方法(shortest path for video summary,SPVS)。SPVS方法将视频摘要问题转化为最短路径求解问题,路径上的帧即为关键帧,在数据集上的大量主客观实验结果都表明其具有良好的性能。
文摘With the wider growth of web-based documents,the necessity of automatic document clustering and text summarization is increased.Here,document summarization that is extracting the essential task with appropriate information,removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task.In this research,a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation(gFAR).Initially,the graph model is used to map the relationship among the data(multi-source)followed by the establishment of document clustering with the generation of association rule using the fuzzy concept.This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy.This framework is provided in an interpretable way for document clustering.It iteratively reduces the error rate during relationship mapping among the data(clusters)with the assistance of weighted document content.Also,this model represents the significance of data features with class discrimination.It is also helpful in measuring the significance of the features during the data clustering process.The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns(RRP),ROUGE score,and Discrimination Information Measure(DMI)respectively.Here,DailyMail and DUC 2004 dataset is used to extract the empirical results.The proposed gFAR model gives better trade-off while compared with various prevailing approaches.
