SE4ExSum: An Integrated Semantic-aware Neural Approach with Graph Convolutional Network for Extractive Text Summarization

Recently, advanced techniques in deep learning such as recurrent neural network (GRU, LSTM and Bi-LSTM) and auto-encoding (attention-based transformer and BERT) have achieved great successes in multiple application domains including text summarization. Recent state-of-the-art encoding-based text summarization models such as BertSum, PreSum and DiscoBert have demonstrated significant improvements on extractive text summarization tasks. However, recent models still encounter common problems related to the language-specific dependency which requires the supports of the external NLP tools. Besides that, recent advanced text representation methods, such as BERT as the sentence-level textual encoder, also fail to fully capture the representation of a full-length document. To address these challenges, in this paper we proposed a novel semantic-ware embedding approach for extractive text summarization, called as: SE4ExSum. Our proposed SE4ExSum is an integration between the use of feature graph-of-words (FGOW) with BERT-based encoder for effectively learning the word/sentence-level representations of a given document. Then, the graph convolutional network (GCN) based encoder is applied to learn the global document's representation which is then used to facilitate the text summarization task. Extensive experiments on benchmark datasets show the effectiveness of our proposed model in comparing with recent state-of-the-art text summarization models.