Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm

Abstract

The significance of text summarization in the Natural Language Processing (NLP) community has now expanded because of the staggering increase in virtual textual materials. Text summary is the process created from one or multiple texts which convey important insight in a little form of the main text. Multiple text summarization technique assists to pick indispensable points of the original texts reducing time and effort require reading the whole document. The question was approached from a different point of view, in a different domain by using different concepts. Extractive and abstractive are the two main methods of summing up text. Though extractive summary is primarily concerned with what summary content the frequency of words, phrases, and sentences from the original document should be used. This research proposes a sentence based clustering algorithm (K-Means) for a single document. For feature extraction, we have used Gensim word2vec which is intended to automatically extract semantic topics from documents in the most efficient way possible.