Research on Automatic Summary Methods for Reportable News under the Graph Model Framework
[Purpose/Significance]With the graph model framework,the representation of summary knowledge is an important technical node in the automatic text summarization process.To address the issue of insufficient depth of semantic disclosure of summary content,this paper proposes a model for automatic summarization of news articles,providing a reference for practical research in related fields using summarized web reportable news text data.[Method/Process]With ETM(Embedded Topic Model),a topic model analysis tool integrating word vectors,this paper intro-duced topic importance and semantic relevance features into the topic construction link of the target summary sentence in the graph model framework.And it redesigned the statistical features between reportable news sentences to mine and filter the in-depth topic semantic information of the texts.Based on this,it formed the automatic summary ex-traction model for reportable news under the method proposed in this paper.Subsequently,according to the main func-tional requirement,it proposed a quantitative index system of the summary topic measurement function,and estab-lished the corresponding relationship between the measurement standard and the quantitative method to optimize and adjust the proposed model of reportable news.[Result/Conclusion]Using the graph model framework,the automatic summarization method for reportage news specifically selects the summarization process of agricultural science and technology dynamic reportage news for empirical research,establishes a measurement standard for automatic summa-rization of reportage news,and further obtains an optimized reportage news summarization model scheme.The results show that it performs better than the comparative method in terms of external reportage function and internal ROUGE evaluation,which can effectively improve the accuracy of automatic summarization extraction for reportage news.
graph modelautomatic summary of reportable newsEmbedded Topic Model ETMROUGE evaluation
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