Text summarization using Secretary problem

Abstract

Automatic text summarization is a long-running program, a crucial part of NLP(Natural language processing), which is again a subpart of Artificial Intelligence(AI). Many successful research techniques have been invented for summarization purposes. We propose a unique concept to generate the summary using the secretary problem, which comes under the extractive text summarization method. We divide the document text into two parts. We will match our text with the main title in the first part. If the main title’s words match the document text sentence, maintain those sentences in one of the lists. We will apply the secretary problem in other sentences that do not have title words. Combined with other sentence generation methods, the Secretary problem will guarantee the best candidate one-third of the time or 37%. This article presents our concept of leveraging a mathematical model to generate a summary that does not include some important sentences.