Summarization is one of the key features of human intelligence. It plays an important role in understanding and representation. With rapid and continual expansion of texts, pictures and videos in cyberspace, automatic summarization becomes more and more desirable. Text summarization has been studied for over half century, but it is still hard to automatically generate a satisfied summary. Tradi...

Automatic text summarization is the process of reducing the text content and retaining the important points of the document. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. In this paper, we discuss some of the extractive based text ...

Abstract Live blogs are an increasingly popular news format to cover breaking and live events in online journalism. Online websites around the world using this medium give their readers a minute by update on event. Good summaries enhance value of for reader, but often not available. In article, (a) we first define task summarizing blog, (b) study ways automatically collecting corpora blog summa...

Bayesian Active Learning has had significant impact to various NLP problems, but nevertheless it’s application text summarization been explored very little. We introduce Summarization (BAS), as a method of combining active learning methods with state-of-the-art models. Our findings suggest that BAS achieves better and more robust performance, compared random selection, particularly for small da...

In addition to the familiar tasks of update summarization and automatic summary evaluation, the TAC 2010 summarization track includes a new task: guided summarization. It is designed to “encourage a deeper linguistic (semantic) analysis of the source documents” in order to generate a summary guided by a given set of aspects. The new task is an upgrade of query-focused summarization in that an a...

This paper deals with our past and recent research in text summarization. We went from single-document summarization through multidocument summarization to update summarization. We describe the development of our summarizer which is based on latent semantic analysis (LSA). The classical LSA-based summarization model was improved by Iterative Residual Rescaling. We propose the update summarizati...

Automatic summarization is the creation of a shortened version of a text by a computer program. The product of this procedure still contains the most important points of the original text. The phenomenon of information overload has meant that access to coherent and correctlydeveloped summaries is vital. In this paper the comparison of the four systems of the automatic summarization is held and ...

We study a new content-based method for the evaluation of text summarization systems without human models which is used to produce system rankings. The research is carried out using a new content-based evaluation framework called FRESA to compute a variety of divergences among probability distributions. We apply our comparison framework to various well-established content-based evaluation measu...