Information Overloadrq  is not a new term but with the massive development in technology which enables anytime, anywhere, easy and unlimited access; participation & publishing of information has consequently escalated its impact. Assisting userslq    informational searches with reduced reading surfing time by extracting and evaluating accurate, authentic & relevant information are the primary c...

In this paper, we describe four experiments in text summarization. The first experiment involves the automatic creation of 120 multi-document summaries and 308 single-document summaries from a set of 30 clusters of related documents. We present official results from a multi-site manual evaluation of the quality of the summaries. The second experiment is about the identification by human subject...

Automatic text summarization is a technique concerning the creation of a compressed form for single document or multidocuments. The summary creation under the condition of the redundancy and the summary length limitation is a challenge problem. The automatic text summarization system which is built based on exploiting of the advantages of different resources in form of an integration model coul...

This paper presents a semantic graph-based method for extractive summarization. The summarizer uses WordNet concepts and relations to produce a semantic graph that represents the document, and a degree-based clustering algorithm is used to discover different themes or topics within the text. The selection of sentences for the summary is based on the presence in them of the most representative c...

The 2013 Association for Computational Linguistics MultiLing Pilot posed a task to measure the performance of multilingual, single-document, summarization systems using a dataset derived from many Wikipedias. The objective of the pilot was to assess automatic summarization of multilingual text documents outside the news domain and the potential of using Wikipedia articles for such research. Thi...

Aiming at the difficulties in document-level summarization, this paper presents a two-stage, extractive and then abstractive summarization model. In first stage, we extract important sentences by combining similarity matrix (only used for time) or pseudo-title, which takes full account of features (such as sentence position, paragraph more.). To coarse-grained from document, considers different...

Corresponding Author: Yogan Jaya Kumar Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia Email: [email protected] Abstract: It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document t...

Document summarization is a technique aimed to automatically extract the main ideas from electronic documents. With the fast increase of electronic documents available on the network, techniques for making efficient use of such documents become increasingly important. In this paper, we propose a novel algorithm, called TriangleSum for single document summarization based on graph theory. The alg...

Extractive text summarization is the process of selecting relevant sentences from a collection of documents, perhaps only a single document, and arranging such sentences in a purposeful way to form a summary of this collection. The question arises just how good extractive summarization can ever be. Without generating language to express the gist of a text – its abstract – can we expect to make ...

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...