Design and Development of Multi-Document Text Summarisation Framework Using Syntax-aware Region Attention-based BERT Integrated with Adaptive Dilated Res-LSTM

Background: Text summarisation is a procedure that shortens the original source material and separates out important details. Moreover, multi-document text summarisation through integrating the abstraction and extraction approaches is incomplete and remains a major difficult research issue. However, effectively learning the exact meaning of the summarised text is still hard. Therefore, it is significant to address the complications of the standard multi-document text summarisation models. Aim: A novel framework is introduced by employing deep learning approaches. In the beginning, the required text data employed for the validation are collected from the internet resources. Methodology: Initially, the pre-processed text is offered to the text summarisation stage. In this phase, the developed framework utilised Syntax-aware Region Attention-based Bidirectional Encoder Representations from Transformers Integrated with Adaptive Dilated Residual Long Short-Term Memory (SR-B-ADRLNet). Moreover, the parameter of SRA-BERT-I-ADRLSTM is tuned using an Enhanced Uniform Random Variable-based Waterwheel Plant Algorithm (EURV-WPA) for attaining the multi-document text summarisation outcomes. Results: From the validation results, the cosine similarity rate of the developed multi-summarisation model is 0.6205. Conclusion: Thus, the developed multi-document summarisation model using a deep learning model is an efficient tool that produces an informative and succinct summary from a source material.