Optimizing Deep Learning Architecture for Scalable Abstractive Summarization of Extensive Text Corpus

Abstract

Text processing plays a prominent role in dealing with the evergrowing volume of information available on the internet as well as digital platforms. Abstractive text summarization and categorization, in particular, aims to generate concise and coherent summaries by paraphrasing the source text while preserving its core meaning and context. This research work focuses on enhancing abstractive text summarization and categorization for the large corpora through the application of a robust deep neural network architecture. With the increasing volume of information available, the need for efficient summarization techniques becomes critical. A pre-training strategy using diverse datasets is employed to improve the model’s statistical performance and generalization capabilities. Furthermore, to address the challenge of information overload, an attention-based content selection mechanism is introduced, which highlights essential information from the source text to guide this process. The model’s effectiveness is also extended to multi-document summarization, ensuring coherence across related documents. To evaluate the performance, various statistical performance metrices are exploited. In order to judge the novelty of adapted strategy, a benchmarking has been carried out with some state-of-the-art existing frameworks. The obtained results demonstrate the significant potential of this approach in effectively summarizing large corpora and managing the overwhelming amount of textual data available.