PLSGA:Phase-Wise Long Text Summary Generation Approach
Aiming at the problem that the existing methods have difficulty in processing redundant information and can-not select the highest quality abstract when dealing with long text,this paper proposes a staged long text abstract genera-tion method(PLSGA).Firstly,the paper segments the text of the sample data and the reference summary,and uses Sentence-BERT to compare and extract the key information of the text.The paper trains the extraction model through key in-formation and non-key information to retain the semantic information of the original text as much as possible.The extract-ed key information and reference summaries are input as samples into the backbone model BART for generative model train-ing.Finally,multiple candidate summaries are generated through the generative model,and the best-quality summaries are selected using the no-reference summaries scoring model.The experiment proves that the proposed stage-based long text summary generation method has been tested on multiple Chinese long text data sets.The results show that compared with the current mainstream method and ChatGPT,its effect has been improved,having domain advantages,and the quali-ty of the generated summary is much better and more readable.
text summarizationSentence-BERTkey informationBARTno-reference summarization scoring model
万方数据
维普
