This study proposes a novel semantic graph embedding-based abstractive text summarization technique for the Arabic language, namely SemG-TS. SemG-TS employs deep neural network to produce summary. A set of experiments were conducted evaluate performance and compare results those popular baseline word embedding called word2vec. new dataset was collected experiments. Two evaluation methodologies ...

In the age of big data, there is increasing growth data on Internet. It becomes frustrating for users to locate desired data. Therefore, text summarization emerges as a solution this problem. summarizes and presents with gist provided documents. However, summarizer systems face challenges, such poor grammaticality, missing important information, redundancy, particularly in multi-document summar...

This paper presents our extractive summarization systems at the update summarization track of TAC 2008. We proposed two novel methods, one is based on the information distance theory, and the other is based on the sentence centrality which derives from the centrality concept in the graph theory. The evaluation results show that the two submitted runs are very competitive to generate extractive ...

This paper presents our multi-document summarization system ICTGSP-S at DUC 2007. We propose a new method for representing and summarizing documents by integrating subtopics partition with graph representation. The method starts from the assumption that capturing subtopic structure of document collection is essential for summarization. The evaluation results show the benefit of this approach.

Due to the wide use of Internet and the diversity of information, there is a large amount of information which is available to users. So many techniques have been developed for the access of large amount data quickly and accurately. Text summarization helps in reducing the size of a text while preserving its information content. One of the main drawbacks of Automatic Summarization is the vague ...

Video summarization is useful for many applications such as content skimming and searching. Automatic video summarization is extremely challenging as it often depends on semantic tasks such as determining meaning, causal relationships, and importance of the displayed video events. We present a reliable, crowdsourced solution to video summarization based on human computation that addresses one o...

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...

Multi-document summarization is a process of automatic generation of a compressed version of the given collection of documents. Recently, the graph-based models and ranking algorithms have been actively investigated by the extractive document summarization community. While most work to date focuses on homogeneous connecteness of sentences and heterogeneous connecteness of documents and sentence...

We introduce a graph-based sentence ranking algorithm for extractive summarization. Our method is a version of the LexRank algorithm we introduced in DUC 2004 extended to the focused summarization task of DUC 2006. As in LexRank, we represent the set of sentences in a document cluster as a graph, where nodes are sentences and links between the nodes are induced by a similarity relation between ...