Study on Abstractive Text Summarization Techniques
Parth Rajesh Dedhia, Hardik Pradeep Pachgade, Aditya Pradip Malani, Nataasha Raul, Meghana Naik
Abstract
As there is an increase in the usage of digital applications, the availability of data generated has increased to a tremendous scale. Data is an important component in almost every domain where research and analysis are required to solve the problems. It is available in a structured or unstructured format. Therefore, in order to get corresponding data as per the application’s purpose, easily and quickly from different sources of data on the internet, an online content summarizer is desired. Summarizers makes it easier for users to understand the content without reading it completely. Abstractive Text Summarizer helps in defining the content by considering the important words and helps in creating summaries that are in a human-readable format. The main aim is to make summaries in such a way that it should not lose its context. Various Neural Network models are employed along with other machine translation models to bring about a concise summary generation. This paper aims to highlight and study the existing contemporary models for abstractive text summarization and also to explore areas for further research.