Multistage Mixed-Attention Unsupervised Keyword Extraction for Summary Generation

Abstract

Summary generation is an important research direction in natural language processing. Aimed at the problems of redundant information processing difficulties and an inability to generate high-quality summaries from long text in existing summary generation models, BART is the backbone model, an N + 1 coarse–fine-grained multistage summary generation framework is constructed, and a multistage mixed-attention unsupervised keyword extraction summary generation model is proposed (multistage mixed-attention unsupervised keyword extraction for summary generation, MSMAUKE-SummN). In the N-coarse-grained summary generation stages, a sentence filtering layer (PureText) is constructed to remove redundant information in long text. A mixed-attention unsupervised approach is used to iteratively extract keywords, assisting summary inference and enriching the global semantic information of coarse-grained summaries. In the 1-fine-grained summary generation stage, a self-attentive keyword selection module (KeywordSelect) is designed to obtain keywords with higher weights and enhance the local semantic representation of fine-grained summaries. Tandem N-coarse-grained and 1-fine-grained summary generation stages are used to obtain long text summaries through a multistage generation approach. The experimental results show that the model improves the ROUGE-1, ROUGE-2, and ROUGE-L metrics by a minimum of 0.75%, 1.48%, and 1.25% over the HMNET, TextRank, HAT-BART, DDAMS, and SummN models on summarized datasets such as AMI, ICSI, and QMSum.