Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical relations to address redundancy problem in text summarization. We first examined and redefined the type of rhetorical relations th...

Text summarization since the late 50’s of the 20th century by the simple technical based on term frequency and it applied for technical text summarization at IBM institute. During more than 50 years of development, text summarization is still a hot topic that attracting many researchers, scholars in the field of data mining and natural language processing proposals development of the text summa...

The rapid growth of online information services has created the problem of information explosion. Automatic text summarization techniques are essential for dealing with this problem. The process of compacting a source document to reduce its complexity and length while retaining its most important contents is called text summarization. This paper introduces Parsumist-a text summarization system ...

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...

In this paper, a novel text clustering technique is proposed to summarize text documents. The clustering method, so called ‘Ensemble Clustering Method’, combines both genetic algorithms (GA) and particle swarm optimization (PSO) efficiently and automatically to get the best clustering results. The summarization with this clustering method is to effectively avoid the redundancy in the summarized...

The University of Maryland participated in three tasks organized by the Text Analysis Conference 2008 (TAC 2008): (1) the update task of text summarization; (2) the opinion task of text summarization; and (3) recognizing textual entailment (RTE). At the heart of our summarization system is Trimmer, which generates multiple alternative compressed versions of the source sentences that act as cand...

With the rapid growth of the Internet, most of the textual data in the form of newspapers, magazines and journals tend to be available on-line. Summarizing these texts can aid the users access the information content at a faster pace. However, doing this task manually is expensive and time-consuming. Automatic text summarization is a solution for dealing with this problem. For a given text, a t...

Text summarization aims at getting the most important content in a condensed form from a given document while retains the semantic information of the text to a large extent. It is considered to be an effective way of tackling information overload. There exist lots of text summarization approaches which are based on Latent Semantic Analysis (LSA). However, none of the previous methods consider t...

A summary is a shorter version of the original. Such a simplification highlights the major points from the much longer subject, such as a text, speech, film, or event. The purpose is to help the audience get the gist in a short period of time. Automatic summarization involves reducing a text document or a larger corpus of multiple documents into a short set of words or paragraph that conveys th...

Automatic text summarization tools help users in the biomedical domain to acquire their intended information from various textual resources more efficiently. Some of biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text. However, it seems that exploring other measures rather than the raw frequency for ...