Research on abstractive text summarization based on triplet information guidance

This study introduces the text summarizing model SPOATS, which is driven by fact triples, to solve the issue that the current abstractive text summarization models do not entirely exploit the factual information of text in decoding. The model is based on a Transformer structure, which contains a double encoder capable of extracting facts and a decoder for combining factual features. To begin, the LTP-BiLSTM-GAT (LBiG) model is built and paired with the optimal factual triple selection technique suggested in this paper. The optimal factual triples are then extracted from the unstructured Chinese text to acquire the feature encoding of factual information. Then, the improved S-BERT model is used to represent the original text at the sentence-level vector to obtain the semantic rich sentence encoding. Finally, an attention-based fact fusion mechanism is designed to fuse the dual-encoding features, which can improve the ability of the model to select factual information in the decoding stage. The experimental results show that the proposed model has improved the value of ERPG by 2.0% compared to the baseline model on the dataset LCSTS, and the summary quality has been significantly improved. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.