ANALYSIS FOR TEXT SUMMARIZATION ALGORITHMS FOR DIFFERENT DATASETS

Abstract

With the exponential increase in the data available on the internet for a single domain, it is difficult to understand the gist of a whole document without reading the whole document. Automatic Text Summarization reduces the content of the document by presenting important key points from the data. Extracting the major points from the document is easier and requires less machinery than forming new sentences from the available data. Research in this domain started nearly 50 years ago from identifying key features to rank important sentences in a text document. The main aim of text summarization is to obtain human quality summarization, which is still a distant dream. Abstractive Summarization techniques uses dynamic wordnet corpus to produce coherent and succinct summaries. Automatic text summarization has applications in various domains including medical research, legal domain, doctoral research, documents available on internet etc. To serve the need of text summarization, numerous algorithms based on different content selection and features using different methodologies are made in last half century. Research started from Single document summarization has shifted to Multi-document summarization in last few decades in order to save more time and compressing the same domain documents at once. Here, An analysis is presented on the Single document and Multi-document summarization algorithms on different domain datasets.