基于细粒度可解释矩阵的摘要生成模型

针对摘要模型中总结并解释长篇上下文信息存在的困难,提出一种基于细粒度可解释矩阵,先抽取再生成的摘要模型(fine-grained interpretable matrix,FGIM),提升长文本对显著度、更新性和相关度的可解释抽取能力,引导系统自动生成摘要.该模型通过一个句对判别(pair-wise)抽取器对文章内容进...

Abstractive Summarization Based on Fine-Grained Interpretable Matrix

According to the great challenge of summarizing and interpreting the information of a long article in the summary model. A summary model (Fine-Grained Interpretable Matrix, FGIM), which is retracted and then generated, is proposed to improve the interpretability of the long text on the significance, update and relevance, and then guide to automatically generate a summary. The model uses a pair-wise extractor to compress the content of the article, capture the sentence with a high degree of centrality, and uses the compressed text to combine with the generator to achieve the process of generating the summary. At the same time, the interpretable mask matrix can be used to control the direction of digest generation at the generation end. The encoder uses two methods based on Transformer and BERT respectively. This method is better than the best baseline model on the benchmark text summary data set (CNN/DailyMail and NYT50). The experiment further builds two test data sets to verify the update and relevance of the abstract, and the proposed model achieves corresponding improvements in the controllable generation of the data set.