Abstractive Text Summarization using Seq2seq Model

Knowledge is power and information is liberating. As the quote says, in today's world the information is available in abundance and a lot of new possibilities can be explored from them. Text summarization is one of the main applications of natural language processing. Text summarization is one of the widely used methods to process the text corpus and obtain a precise text that captures the entire context and preserves the important information conveyed through the text. This paper presents an approach of abstractive text summarization using the seq2seq model. The proposed methodology aims at enhancing the efficiency of the summary generated with the help of the data augmentation technique. The summary comprises new words and sentences thereby improving the quality of it. For evaluating the quality of summarization bilingual evaluation understudy (BLEU) score is used.