Deep Learning Model for Abstractive Automatic Text Summarization in Hindi
Keywords:
Recurrent Neural Network, long short-Term Memory, Term Frequency, Inverse Document Frequency, Word Embedding, Word Vector, Continuous Bag of wordsAbstract
Text summarization is a process in which long texts are compressed and condensed into smaller summaries. Only the crux ideas of the document are fetched from the main document and included in the final piece, which is cohesive. As the amount of data is soaring exponentially. The need for a tool that summarizes text specifically for Indian languages is also pertinent. Using a variety of techniques, we strive to construct both extractive and abstractive approaches for text summarization of Hindi text in this research. The abstractive method is based on seq-to-seq networks and the attention model. A summary of all Indian regional languages cannot be generalized by a single approach. This is so that each language may be treated separately because every language has unique linguistic characteristics.
