Text Summarization Model Based on Transformer-TextRank-PGN
Aiming at the problem of insufficient understanding of text semantic information by the encoder and uncontrollable words generated by the decoder in the text summarization task,a Transformer-TextRank-PGN text summarization model is pro-posed,which retains the advantage of both generative summary and extractive summary.The TextRank algorithm is introduced on the encoder side of the model to enhance the ability of the encoder to learn the semantic information of the text.The decoder side in-troduces a pointer network to point to the extracted words from the original text.The probability distribution of the extracted words and the probability distribution of the words generated by the decoder together affect the final generated words.The model can repro-duce the original text details and generate OOV vocabulary.The experimental results on the NLPCC text summarization dataset show that the accuracy and readability of the summaries generated by the model are closer to the standard summaries given in the data set.
国家科技期刊平台
NSTL
万方数据
