Abstract: 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 co-occurrence 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. 

Key words: automatic summary generation, TextRank, co-occurrence keyword, MMR algorithm, word2vec model