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
Generation-based linguistic steganography is a popular research area of information hiding.The text generative steganographic method based on conditional probability coding is the direction that researchers have recen...Generation-based linguistic steganography is a popular research area of information hiding.The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to.However,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in concealment.In this paper,we propose a topic-controlled steganography method which is guided by graph-to-text generation.The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is controllable.We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation.Different from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional probability.We test our method in different aspects and compare it with other text generative steganographic methods.The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.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
产业集群能够为企业提供创新动力和竞争优势。我国产业集群起步较晚、总体发展水平不高、创新主体之间的联系不紧密等问题对后续研究提出了更多的要求。通过对产业集群创新网络相关文献开展系统评估与分析,为学者后续开展理论研究、提...产业集群能够为企业提供创新动力和竞争优势。我国产业集群起步较晚、总体发展水平不高、创新主体之间的联系不紧密等问题对后续研究提出了更多的要求。通过对产业集群创新网络相关文献开展系统评估与分析,为学者后续开展理论研究、提高产业集群网络创新效率、政府制定产业集群创新发展政策提供更多参考意见。基于2000—2021年CNKI数据库中的550篇中文文献和Web of Science核心合集数据库中的511篇英文文献,运用CitespaceⅤ、知网可视化分析、WOS检索结果分析、文献计量在线分析平台等方法提炼出该领域的研究热点、研究来源与研究演化趋势。在全面梳理该领域重点期刊文献的基础上,揭示产业集群创新网络的四大研究主题:网络形成演化与动力机制、网络发展模式与路径升级、基于主体视角的“网络嵌入特征—知识—绩效”研究、基于地理空间视角的产业集群区域创新网络发展研究。最后,从研究视角、研究对象、研究方法3个方面为未来研究提出具体建议。展开更多
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 in part by the National Natural Science Foundation of China [62102136]the 2020 Opening Fund for Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering [2020SDSJ06]the Construction Fund for Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering [2019ZYYD007].
文摘Generation-based linguistic steganography is a popular research area of information hiding.The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to.However,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in concealment.In this paper,we propose a topic-controlled steganography method which is guided by graph-to-text generation.The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is controllable.We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation.Different from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional probability.We test our method in different aspects and compare it with other text generative steganographic methods.The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.
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.
文摘产业集群能够为企业提供创新动力和竞争优势。我国产业集群起步较晚、总体发展水平不高、创新主体之间的联系不紧密等问题对后续研究提出了更多的要求。通过对产业集群创新网络相关文献开展系统评估与分析,为学者后续开展理论研究、提高产业集群网络创新效率、政府制定产业集群创新发展政策提供更多参考意见。基于2000—2021年CNKI数据库中的550篇中文文献和Web of Science核心合集数据库中的511篇英文文献,运用CitespaceⅤ、知网可视化分析、WOS检索结果分析、文献计量在线分析平台等方法提炼出该领域的研究热点、研究来源与研究演化趋势。在全面梳理该领域重点期刊文献的基础上,揭示产业集群创新网络的四大研究主题:网络形成演化与动力机制、网络发展模式与路径升级、基于主体视角的“网络嵌入特征—知识—绩效”研究、基于地理空间视角的产业集群区域创新网络发展研究。最后,从研究视角、研究对象、研究方法3个方面为未来研究提出具体建议。
