Abstractive Summarization Based on Fine-grained Interpretable Matrix

WANG Haonan1, GAO Yang 1,3,†, FENG Junlan2, HU Min2, WANG Huixin2, BAI Yu1

1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081; 2. China Mobile Research Institute, Beijing 100032; 3. Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing 100081; † Corresponding author, E-mail: gyang@bit.edu.cn

Abstract 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. Key words abstractive summarization; interpretable extraction; centrality; mask matrix; controllable