Analysis of Abstractive Text Summarization with Deep Learning Technique

In today's era, data in textual format has got great importance and is used to extract useful information from this data to design various kinds of information systems such as Document Generation, Prediction systems, Report Generation, Recommendation Systems, and Language modeling, and many more. That is why such techniques are very important, which will reduce the amount of data while saving the information and various parameters concerning this information. One such technique is text summarization which retains essential and useful information. This technique is very simple and convenient as compared to other techniques of summarization. For processing data, the Apache tool of Kafka is used. This platform is useful for real-time streaming data pipelines and many applications related to it. With this, one can use APIs of native Apache Kafka to populate data lakes, stream variants to and from databases, and power machine learning and analytically carry out. The input portion in this situation is a spark base platform for analytics. For the fast development of workflows for complex machine learning systems, Tensorflow is evolved as a significant library of machine learning.

Keywords: Abstractive Summarization, Apache Kafka, Azure ML, Extractive Summarization, MemSQL, Tensorflow, Text Summarization