A Comprehensive Survey on Abstractive Text Summarization of Devanagari Script Based Hindi Language
Keywords:
abstractive text summarization, Devanagari script, Hindi and Marathi, generated summaries, neural network, and transfer learning.Abstract
With the exponential growth of digital content in Indic languages, there is an increasing demand for advanced Natural Language Processing ((NLP) techniques tailored to specific scripts. The research explores the landscape of abstractive text summarization in Devanagari script, with a particular emphasis on Hindi and Marathi, two prominent languages that utilize this script. The survey begins by providing an overview of abstractive text summarization techniques, highlighting the challenges and opportunities specific to languages using the Devanagari script which delves into the existing methodologies, models, and datasets used for abstractive summarization in Hindi and Marathi, offering insights into the unique linguistic features that impact summarization tasks. Furthermore, the survey discusses the impact of linguistic nuances such as compound words, inflections, and contextual dependencies on the efficiency of abstractive summarization models. The research reviews the major advancements in neural network (NN) architectures and pre-trained language models applied to abstractive summarization which analyzes the strengths and limitations of these models, considering factors like model size, training data, and computational resources. In addition to model-centric approaches, the survey explores the role of domain-specific datasets and transfer learning in enhancing the performance of abstractive summarization systems for Devanagari-script languages that also sheds light on the evaluation metrics and benchmarks commonly employed to measures the generated summary qualities, and addressing the challenges of cross-lingual evaluation.
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