Improving Multi-Document Summarization with GRU-BERT Network

Abstract

Multi-document summarization has been a challenging task due to the difficulties in capturing essential information from multiple sources and generating coherent and non-redundant summaries. In this proposed model, we address these challenges by leveraging the power of two popular natural language processing techniques, Bidirectional Encoder Representations from Transformers (BERT) and Gated Recurrent Unit (GRU). The Document Understanding Conference (DUC) dataset, a widely recognized benchmark dataset for multi-document summarization, was used to train and evaluate the model. By using BERT to generate contextual embeddings and GRU to capture sequence information, the proposed method outperforms previous methods in terms of summarization quality metrics such as ROUGE (RecallOriented Understudy for Gisting Evaluation). The proposed model has significant potential for use in various applications, such as news summarization, document summarization, and automated content creation. This study demonstrates that combining BERT and GRU models can effectively capture the contextual and sequential information in multi-document summarization, leading to high-quality summaries that overcome the limitations of previous methods.