Hindi Text Summarization Using Sequence to Sequence Neural Network

Abstract

Text summarizing reduces a large block of text data to a precise, short, and intelligible text that conveys the whole meaning of the actual text in a few words while maintaining the original context. Due to a lack of relevant summaries, it is hard to understand the main idea of the document. Text summarization using the abstractive technique is well-studied in English, although it is still in its infancy in Indian regional languages. In this study, we investigate the effectiveness of using a sequence-to-sequence (Seq2Seq) neural network based on attention and its optimization for text summarization for the Hindi language (HiATS), explicitly comparing the Adam and RMSprop optimizers. Our method allows the model to take the Hindi language dataset and, as output, provides a concise summary that accurately reflects the gist of the original text. The performance of the models will be evaluated using Rouge-1 and Rouge-2 metrics.