Text Summarization Based on Improved Pointer-Generator Network
With people receive more and more information every day,it's important to be able to get the information we want in a short period of time,so text summaries have become indispensable.Manually generating abstracts for articles is a time-consum-ing and laborious task,and it becomes necessary to automatically generate abstracts with high readability and fluency.There are many ways to generate abstracts,which are divided into extractive abstracts and generative abstracts.The pointer generation net-work is still a very popular text summarization method because it can effectively solve the problem of unregistered words.In our work,the traditional pointer generation network is still used as the basic framework,and the encoder part in Transformer is intro-duced as preprocessing to improve the coding quality.In addition,the penalty of unregistered words is introduced to improve the novelty of the generated abstract text.Experimental results show that the model has achieved good results on the NLPCC data set.
国家科技期刊平台
NSTL
万方数据
