Enhancing Abstractive Text Summarization using Two-Staged Network for Telugu Language (EATS2N)
Keywords:
Summarization, Extractive Summary, Keywords, Telugu News Paper, Biology TextbookAbstract
The process of producing a clear and short synopsis of lengthy texts without sacrificing the overall meaning by concentrating on the passages that provide important information is known as text summarization Extractive summaries that highlight a significant portion of the input texts frequently include crucial keywords. The vast majority of strategies for extractive summarization are based on the idea of locating keywords and extracting sentences that have a disproportionately high number of keywords compared to the others. The process of extracting keywords often involves identifying relevant terms that occur more frequently than other words and putting an emphasis on the most significant of them.Selecting keywords manually is challenging, susceptible to inaccuracies, and demands considerable time and attention.A technique that can automatically extract keywords from Telugu e-newspaper datasets was proposed by using this work. The keywords may then be used for text summarizing. The proposed method compares two different datasets, the telugu newspaper and the biology text book and the performance metrics are compared using the accuracy and ROGUE score values.
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