Abstract: Abstractive summarization is a crucial task in natural language processing that aims to generate concise and informative summaries from a given text.Deep learning-based sequence-to-sequence models have become the mainstream approach for generating abstractive summaries,achieving remarkable performance gains.However,existing models still suffer from issues such as semantic ambiguity and low information content due to the lack of attention to the dependency relationships between key concepts and sentences in the input text.To address this challenge,the keywords guided heterogeneous graph model for abstractive summarization is proposed.This model leverages extracted keywords and constructs a heterogeneous graph with both keywords and sentences as input to model the dependency relationships between them.A document encoder and a graph encoder are respectively used to capture textual information and dependency relationships in the heterogeneous graph.Moreover,a hierarchical graph attention mechanism is introduced in the decoder to improve the model's attention to significant information when generating summaries..Extensive experiments on the CNN/Daily Mail and XSum datasets demonstrate that the proposed model outperforms existing methods in terms of the ROUGE evaluation metric.Human evaluations also reveal that the generated summaries by the proposed model contain more key information and are more readable compared to the baseline models.

Key words: Abstractive summarization, Keywords, Heterogeneous graph, Graph attention, Sequence to sequence model