Until recently there are no common, convenient, and repeatable evaluation methods that could be easily applied to support fast turn-around development of automatic text summarization systems. In this paper, we introduce an informationtheoretic approach to automatic evaluation of summaries based on the Jensen-Shannon divergence of distributions between an automatic summary and a set of reference...

Many previous research studies on extractive text summarization consider a subset of words in a document as keywords and use a sentence ranking function that ranks sentences based on their similarities with the list of extracted keywords. But the use of key concepts in automatic text summarization task has received less attention in literature on summarization. The proposed work uses key concep...

by MEGHANA VISWANATH (Under the Direction of Krzysztof J. Kochut) ABSTRACT This thesis presents an ontology-based approach to automatic extractive summarization of text. Most of the extractive summarization systems so far have used statistical importance measures to determine importance of sentences. We use a knowledge-based approach which makes use of ontological knowledge to determine sentenc...

Due to the rapid-developed Internet and with the big data era coming, the automatic summarization research has been emerged a popular research topic. The aim of automatic summarization is in attempt to select important text or spoken sentence to represent the topic (theme) of original text or spoken document according to a predefined summarization ratio. In this study we frame automatic summari...

Abstract—Propelled by the modern technological innovations data and text will be more abundant throughout year. With this much text, automatic summarization is needed now than ever to help summarize a text. Automatic defined as creation of shortened version computer program, product procedure still contains most important points original Statistical approaches one method. There 5 statistical th...