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The task of generating news text summarizations aims to solve the problems of wasting time and read- ing fatigue caused by users'inability to quickly grasp the key points of the content when reading news. At present, the best text summarization model for Chinese is the T5 PEGASUS model, but there are few researches on this model. In this study, the Chinese word segmentation of the T5 PEGASUS model is improved, and the Pkuseg word segmentation method, which is more suitable for news field, is used for processing, and its effectiveness is verified on three public datasets with different news lengths: NLPCC2017, LCSTS and SogouCS. It is found that the Pkuseg method is more suitable for the T5 PEGASUS model. The ROUGE value of T5 Pegasus model generated summaries is positively correlated with the length of news text, and the loss value of training set and the decline speed of loss value are negatively correlated with the length of news text. In the face of a small number of training sets, the model can get a high ROUGE score, so the model has a strong few - shot learning ability. [ABSTRACT FROM AUTHOR]

Existing abstractive text summarization models lack attention to keyword information, which leads to the loss of key information in the input text. A keyword semantic information enhancement pointer-generator networks, named KSIE-PGN, is proposed. Firstly, the keyword selection model KSBERT is built to extract keywords. Secondly, a keyword-masked coverage mechanism based on the information of keywords is proposed. When using the coverage mechanism, the continuous attention to keywords in the decoding process is retained. Then, the KSIE-PGN model integrates keyword information in the decoding process including the keyword semantic vector and the keyword context vector. Therefore, the decoder can avoid losing the key information in the input text. The experimental results on the CNN/Daily Mail dataset show that the model can capture the key information in the input text well. [ABSTRACT FROM AUTHOR]

The traditional Encoder-Decoder model with attention mechanism has such problems as text redundancy, inconsistent representation and out of vocabulary (OOV) in the application of the summary task, resulting in low accuracy of the generated summary. The transformer model with ambeddable text location information was improved, pointer network was introduced to help with decoding, and take advantage of pointer network to generated text to generate summary. The effectiveness of the transformer model was verified on the LCSTS Chinese short text summary data set. The results show that the model outperforms the benchmark model by an average of two points in ROUGE scores, and the prominence of the generated content and the fluency of the language are significantly improved while ensuring the consistency of the summary with the input text. [ABSTRACT FROM AUTHOR]

According to the problem that the model in the generative text summarization doesn’t fully understand the semantics of the text and the generated summary lacks key information, this paper proposed a Key-BERT-Pen model that integrated keywords in Chinese abstract generation. The KBPM first used the TextRank method to extract the keywords in the text, and then obtained a more accurate contextual representation through the BERT pre-training model using the extracted keywords and the original text. Finally, it input the obtained word vector into a pointer model with a dual attention mechanism, and the pointer model took vocabularies from the vocabulary or original text to generate the final abstract. Experimental results show that the KBPM can generate text summaries with better readability and higher ROUGE scores. The comparative analysis also verifies that the KBPM effectively solves the problem of the lack of key information in the generated abstract. [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]

Traditional seq2seq model didn' t fully utilize the sentence structure of the input text, this results in that seq2seq model can't generate a text summary with rigorous structure and better grammar. Therefore, the paper proposed a summary generation method with basic structure of sentence. First of all, it explicitly proposed the concept of generating a text summary with sentence structure. Furthermore, it added a sentence structure module and a supervisor module on the summary generator. The sentence structure module captured a series of sentence structures from the input text, the supervisor module evaluated the importance of the sentence structures, this assisted the sentence structure module to capture some available sentence structures. Finally, the summary generator combined the available sentence structures to generate the final summary. The experiments on LCSTS show that its ROUGE scores are higher than seq2seq. In other words,its accuracy surpasses seq2seq. Experiments also verify the necessity of the existence of the sentence structure module and the supervisor module. [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]