Abstractive text summarization of low-resourced languages using deep learning

Abstract

Background Humans must be able to cope with the huge amounts of information produced by the information technology revolution. As a result, automatic text summarization is being employed in a range of industries to assist individuals in identifying the most important information. For text summarization, two approaches are mainly considered: text summarization by the extractive and abstractive methods. The extractive summarisation approach selects chunks of sentences like source documents, while the abstractive approach can generate a summary based on mined keywords. For low-resourced languages, e.g., Urdu, extractive summarization uses various models and algorithms. However, the study of abstractive summarization in Urdu is still a challenging task. Because there are so many literary works in Urdu, producing abstractive summaries demands extensive research. Methodology This article proposed a deep learning model for the Urdu language by using the Urdu 1 Million news dataset and compared its performance with the two widely used methods based on machine learning, such as support vector machine (SVM) and logistic regression (LR). The results show that the suggested deep learning model performs better than the other two approaches. The summaries produced by extractive summaries are processed using the encoder-decoder paradigm to create an abstractive summary. Results With the help of Urdu language specialists, the system-generated summaries were validated, showing the proposed model’s improvement and accuracy.

Text data augmentation and pre-trained Language Model for enhancing text classification of low-resource languages

“…In addition, this methods are direct answer to tasks arising from LRLs., mirroring a broader academic and societal interest in fostering inclusivity and diversity in digital communication systems. The issue of LRLs has emerged as a focal point of inquiry and engagement within the scientific community ( Shafiq et al, 2023 ; Karyukin et al, 2023 ; Sazzed, 2021 ; Ramponi et al, 2022 ; Farooq et al, 2023 ). Addressing the challenges posed by LRLs is seen as a pivotal step towards achieving linguistic equity in the digital domain.…”

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

“…They found that AraT5 and AraGPT2 performed better than other language models. (Shafiq et al, 2023) proposed abstractive summarization of Urdu. They used multilayer encoder and single layer decoder-based transformer.…”

Multi-granularity adaptive extractive document summarization with heterogeneous graph neural networks

“…As a result, several methodologies have integrated multi-granular information with graph models to amplify text summarization effectiveness. In the present study, a sentence-topic-word structure has been established, proving instrumental in addressing cross-sentence dependencies for both single-document and multi-document summarization tasks ( Chen, 2023 ; Mao et al, 2021 ; Shafiq et al, 2023 ). Moreover, an approach employing adaptive breadth and depth has been implemented to update nodes of varied granularity, thereby facilitating a more effective aggregation of node-specific information.…”