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.
