TAAM: Topic-aware abstractive arabic text summarisation using deep recurrent neural networks

ivetext summarisation is essential to producing natural language summaries with main ideas from large text documents. Despite the success of English language-based abstractive text summarisation models in the literature, they are limitedly supporting the Arabic language. Current abstractive Arabic summarisation models have several unresolved issues, a critical one of which is syntax inconsistency, which leads to low-accuracy summaries. A new approach that has shown promising results involves adding topic awareness to a summariser to guide the model by mimicking human awareness. Therefore, this paper aims to enhance the accuracy of abstractive Arabic summarisation by introducing a novel topic aware abstractive Arabic summarisation model (TAAM) that employs a recurrent neural network. Two experiments were conducted on TAAM: quantitative and qualitative. Based on a quantitative approach using ROUGE matrices, the TAAM model achieves 10.8% higher accuracy than other existing baseline models. Additionally, based on a qualitative approach that captures users' perspectives, the TAAM model is capable of producing a coherent Arabic summary that is easy to read and captures the main idea of the input text. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).