Text summarization system can save the time for user when reading large number of documents. The summary of text summarization system usually composed of meaningful sentence which represent content of text. The relations between keyword usually come from their cooccurrences in document. This study using hierarchical clustering method cluster sentences and apply concept formal analysis to find o...

It has been said for decades (if not centuries) that more and more information is becoming available and that tools are needed to handle it. Only recently, however, does it seem that a sufficient quantity of this information is electronically available to produce a widespread need for automatic summarization. Consequently, this research area has enjoyed a resurgence of interest in the past few ...

Automatic Text Summarization was added as a major research thrust of the TIPSTER program during TIPSTER Phase III, 1996-1998. It is a natural extension of the previously supported research efforts in Information Extraction (IE) and Information Retrieval (IR). There is considerable interest in automatically producing summaries due, in large part, to the growth of the Internet and the World Wide ...

We present results from evaluations of an automatic text summarization technique that uses a combination of Random Indexing and PageRank. In our experiments we use two types of texts: news paper texts and government texts. Our results show that text type as well as other aspects of texts of the same type influence the performance. Combining PageRank and Random Indexing provides the best results...

Automatic text summarization means finding out the summary of one or more document by a computer program. The output text or the summary should contain the most important points of the original text without changing its meaning. In this report, we present an extractive approach of Text summarization of Assamese, a free word order inflectional Indic language, using WordNet. From our experiment, ...

In this paper we address the automatic summarization task. Recent research works on extractive-summary generation employ some heuristics, but few works indicate how to select the relevant features. We will present a summarization procedure based on the application of trainable Machine Learning algorithms which employs a set of features extracted directly from the original text. These features a...

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of...

The exploitation of the discourse structure of a text and the identification of the discourse categories are essential elements for the automatic summarization, as well as for the textual information retrieval. In this paper we will describe an automatic summarization strategy that uses these elements as the basis for the extraction of the most relevant textual segments that will constitute the...

We present results from an eye tracking study of automatic text summarization. Automatic text summarization is a growing field due to the modern world’s Internet based society, but to automatically create perfect summaries is challenging. One problem is that extraction based summaries often have cohesion errors. By the usage of an eye tracking camera, we have studied the nature of four differen...