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Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional TextRank algorithm only considers the similarity between sentences but neglects the similarity between articles themselves when generating summaries, and the generated summaries often contain repeated expressions of information. Therefore, a TextRank algorithm based on cooccurrence keywords is proposed. The article is represented as a sentence vector by word2Vc model. Considering the category of the article, the co-occurrence keywords of this kind of article are taken as parameters to participate in the iterative calculation of sentence weight. The sentence weight obtained by iteration is corrected by sentence length, keyword number and other information. The experimental results show that the proposed algorithm can improve the comprehensiveness and accuracy of the summary generation. At the same time, this algorithm uses MMR to remove the redundancy of abstracts, which improves the problem of repeated representation of abstracts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As an important supporting technology for applications such as knowledge base construction and information retrieval, entity disambiguation plays an important role in the field of Natural Language Processing (NLP). However, in the short text environment, it is difficult for entity disambiguation to extract sufficient context features for disambiguation. Aiming at the characteristics of short texts, this paper proposes a disambiguation method of graph models based on entity topic relations. This method uses TextRank algorithm to infer the topic of corpus constructed by knowledge base information, and uses the result of topic inference as the representation of relationship between entities. By combining the disambiguation score given by the semantic matching model based on BERT, the disambiguation network graph is constructed, and the final disambiguation result is obtained through search and sorting. The data set provided in the short text entity link task of CCKS2020 is used to evaluate the method. The experimental results show that the proposed method is better than other entity linking methods in entity disambiguation of short text, and can effectively solve the entity disambiguation problem of Chinese short text. [ABSTRACT FROM AUTHOR]

Aiming at the problem that TextRank ignores syntactic information and topic information when extracting chapter keywords, propose a chapter keyword extraction model based on syntactic analysis and topic distribution. Model includes two stages of chapter keyword extraction: paragraph and chapter. Firstly, use paragraphs as a unit to extract paragraph keywords by combining word co-occurrence, grammatical and semantic information. Then cluster the paragraphs according to the paragraph topics to form the paragraph topic set. Finally, extract chapter keywords based on the distribution characteristics of paragraph topics. On the open news dataset, the model’s extraction effect improves by about 10% compared with the original TextRank. Results show that the method has significantly improved the extraction effect, and prove the importance of grammatical information and topic information. [ABSTRACT FROM AUTHOR]

Aiming at the traditional graph models for text summarization only focus on statistical features or shallow semantic features, and lack mining and utilization of deep topic semantic features, this paper proposed MDSR( multi-dimension summarization rank), an automatic text summarization method that combined topic feature. Specifically, this method adopted the LDA model to mine the semantic information of text topics and measured the impact of topic feature on a sentence by defining the importance of the topic. And it improved the construction mode of the probability transition matrix of graph model nodes by combining the topic feature with statistic features and inter-sentence similarity. Finally, it extracted and measured summarization according to the weight of sentence nodes. The results show that the ROUGE value evaluates by MDSR reaches the best when the weight ratio of topic feature, statistic feature and inter-sentence similarity is 3 : 4 : 3. The ROUGE-1, ROUGE-2, ROUGE-SU4 are 53. 35%,35. 18% and 33. 86%, which perform better than other comparisons. It shows that the text summarization method combining topic feature can effectively improve the accuracy of the summarization extraction. [ABSTRACT FROM AUTHOR]

The method of Chinese existing automatic text summarization mainly utilizes the text's own information, and its defect is that it cannot make full use of the related semantic information between the words. Therefore, this paper proposed an improved Chinese text summarization method. This method integrated the information of the external corpora into the TextRank algorithm in the form of a word vector. Combined TextRank with word2vec, it mapped each word in the sentence to the high dimensional lexicon to form a sentence vector. This method fully considered the similarity between sentences, the coverage of keywords and the similarity between sentence and title to calculate the influence weights among sentences, and choose the top-ranked sentences used as the summarization of the text. The results of experiment show that this method achieves good results in the data set of this paper, and is more effective than the original method in extracting Chinese summarization automatically. [ABSTRACT FROM AUTHOR]

This paper studied the influence of lexical semantic difference on TextRank algorithm. and presented a keyword extraction method based on word vector and TextRank. Firstly.it used FastText to represent word vector from the document corpus. Then,based on the idea of implicit subject distribution and used the differences in lexical semantics to build a probability transfer matrix for TextRank. Finally.it iteratively calculated the lexical graph model and extracted keywords. Experimental results show that the extraction performance of this method is significantly improved compared with the traditional method. In addition, it is proved that the use of word vectors can improve the performance of TextRank algorithm simply and effectively. [ABSTRACT FROM AUTHOR]

When abstracting Chinese texts, the traditional TextRank algorithm only considers the similarity between nodes and neglects other important information of the text. Firstly, aiming at Chinese single document, on the basis of existing research, this paper used TextRank algorithm,on the one hand, it considered the similarities between sentences, on the other hand, Text-Rank was combined with the overall structural information of texts and the contextual information of sentences, such as the physical position of the document sentences or paragraph, feature sentences, core sentences and other sentences that might increase the weight of the sentence, all were used to generate the digest candidate sentence group of the text. And then, removing high-similarity sentences by redundancy processing technology on the digest candidate sentence group. Finally, the experimental verification shows that the algorithm can improve the accuracy of the generated digest, indicating the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

As a classical key-word extracting and abstraction auto-generating algorithm, Text Rank considered the text as a group of terms, and sought a latent semantic relationship between terms according to iteratively calculating the weights of the terms in the nodes graph. Based on the nodes graph model of TextRank, combined node graph and Markov state transform model, weighted the edge between nodes with conditional probability, proposed a new nodes graph model and corresponding algorithm TextRank_Revised(TR-R). According to the verification on labeled and unlabeled samples, it shows that without promotion of time complexity, the new algorithm can get a key-word sorting consequence which is closer to the manual than the original algorithm from the single text. [ABSTRACT FROM AUTHOR]

Aiming al the problem of the information extraction of history evolution, this paper proposed a method combined PAM with topical TextRank . PAM could fetch the distribution of different topics and the distribution of topics and words. Based on the distribution modeled by the PAM. topical TextRank could extract the keywords which were more relevant to historical evolution topic. Because historical evolution topic was complex and highly relevant to other topics. W hether the words were relevant to the historical evolution topic was not clear and hardly confirmed. So. PAM got both the distribution of topics and the distribution of topics and words, which was very useful to solve those problems. Furthermore, the historical evolution topic facilitated topical TextRank to extract the words which were more relevant to historical evolution topic. The result shows that the method combined the PAM and topical TextRank is more effective to extract the topic information of history evolution. [ABSTRACT FROM AUTHOR]