AUTO TEXT SUMMARIZATION

With the increasing amount of information, it has become difficult to take out concise information. Thus it is necessary to build a system that could present human quality summaries. Automatic text summarization is a tool that provides summaries of a given document. In this project three different approaches have been implemented for text summarization. 

In all these three summarizers, sentences are represented as a feature vector. In the first approach, features like position of sentences, vocabulary intersections, resemblance to title, sentence inclusion of numerical data are used and model is trained using Genetic Algorithm. In the second approach, apart from the features used in the first approach, structure of the document, popularity of content are also used as features. In the third approach, word2vec algorithm is used to generate summary.