基于语义图优化算法的中文微博观点摘要研究

doi:10.6040/j.issn.1671-9352.1.2016.PC2

Abstract

Abstract: To obtain key information in different topics efficiently, microblog opinion summarization has been a hot spot in natural language processing recently. The baseline method of this paper extracts keywordsusing TF-IDF algorithm, and calculate the importance scores of microblogs to filter out opinion summarization directly; the naive improved methodadded a step of sentiment classification, andremove microblogs which are of low importance and high semantic repetitionusing semantic distance between microblogs to generate opinion summarization;the method based on semantic graph optimization algorithm constructs a complete graph using importance scores and semantic distance of microblogs, and filters out the opinion summarization using graph optimization algorithm. According to the official result of evaluation,on the test dataset of COAE2016, the average ROUGE-1 value, ROUGE-2 value and ROUGE-SU4 value of 10topics using the naive improved methodreached 26.39%, 0.68% and 5.69% respectively, and got 6 max values out of 9 kinds of evaluation index. Besides, the results of experiments done on COAE2016 sample datasetshows that by using the method based on semantic graph optimization algorithmthe ROUGE-1 value, ROUGE-2 value and ROUGE-SU4 value increased by 0.63%, 1.51%, 2.69% respectively.

Key words: microblogssummarization, semantic graph optimization, TF-IDF, sentence similarity

CLC Number:

TP391

ZHANG Cong, PEI Jia-huan, HUANG Kai-yu, HUANG De-gen, YIN Zhang-zhi. Semantic graph optimization algorithm based chinesemicroblog opinion summarization[J].JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 59-65.

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Semantic graph optimization algorithm based chinesemicroblog opinion summarization

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