Summarization Generation Method for Long Text Based on Key Features
In the research of abstract generation based on deep learning,most of the research is focused on short text.Abstract generation of long text faces a series of problems,such as inaccurate generation information,redundant target summary,lack of summary sentences and so on.Taking long text as the research object,starting from the key information such as key words and key sentences,this paper proposes a long text summary generation method based on key features.Firstly,this paper uses the improved keyword extraction algorithm based on LDA to obtain the keywords of the source text,and encodes the keywords into key informa-tion vectors for correlation calculation in the decoding stage.Secondly,the key sentence extraction algorithm based on TextRank is used to extract the key sentence from the source text to realize the compression of the source text.Finally,Bert language model and Transformer model are used,combined with Copy mechanism to generate text summary for compressed text,so as to improve the ac-curacy of summary sentence extraction.Experiments show that the ROUGE scores obtained by the proposed method on Chinese and English data sets are better than the mainstream summary generation methods.
国家科技期刊平台
NSTL
万方数据
