A Comparative Study on Text Summarization Methods

With the advent of Internet, the data being added online is increasing at enormous rate. Businesses are waiting for models that can render some useful information out of this large chunk; and hence our research holds significance. There are various statistical and NLP models that are used and each of them are efficient in their own way. Here, we will be making a comparative study so as to bring out the parameters and efficiency, thus bringing about a deduction as to when and how best we can use a particular model. Text summarization is the technique, which automatically creates an abstract, or summary of a text. The technique has been developed for many years. Summarization is one of the research works in NLP, which concentrates on providing meaningful summary using various NLP tools and techniques. Since huge amount of information is used across the digital world, it is highly essential to have automatic summarization techniques. Extractive and Abstractive summarization are the two summarization techniques available. Lot of research work are being carried out in this area especially in extractive summarization. The techniques involved here are text summarization with statistical scoring, Linguistic Method, Graph based method, and artificial Intelligence.