With the evolution of the Internet and multimedia technology, the amount of text data has
increased exponentially. This text volume is a precious source of information and knowledge …

In addition to use as a text, Indexing and Abstracting in Theory and Practice holds value for
all individuals and institutions involved in training for information retrieval and related …

This work proposes an approach to address the problem of improving content selection in
automatic text summarization by using some statistical tools. This approach is a trainable …

A novel method for simultaneous keyphrase extraction and generic text summarization is
proposed by modeling text documents as weighted undirected and weighted bipartite …

Text summarization is the process of creating a compressed version of a given document
that delivers the main topic of the document. Keyword extraction is the process of extracting …

In existing unsupervised methods, Latent Semantic Analysis (LSA) is used for sentence
selection. However, the obtained results are less meaningful, because singular vectors are …

This work proposes an approach that uses statistical tools to improve content selection in
multi-document automatic text summarization. The method uses a trainable summarizer …

We propose a graph-based method for extractive single-document summarization which
considers importance, non-redundancy and local coherence simultaneously. We represent …

Document summarization plays an increasingly important role with the exponential growth of
documents on the Web. Many supervised and unsupervised approaches have been …

Initial deployment and subsequent dynamic reconfiguration of a software system is difficult
because of the interplay of many interdependent factors, including cost, time, application …