Automatic Summarization of Chinese Long Text Based on Key Information Enhancement
The existing automatic text summarization methods have the problems of redundant key information and low accura-cy in processing long texts.This paper proposes a three-stage text summarization algorithm for Chinese long text.Firstly,the text compression algorithm is used to compress the long text information in a fixed range,and the redundant information irrelevant to the topic is filtered.Then,the deep semantic features of sentences are learned by combining the pre-training model Bert,and the key sentences with rich topic information are further extracted.Finally,the Seq2Seq model with the pointer mechanism is used to rewrite the key sentences.The effectiveness of this algorithm on Chinese long text summarization is verified by experiments based on real large-scale long text data in financial field.
text summarizationlong text compressionpre-trainingsummary generationpointer mechanism
万方数据
维普
