Text summarization is the process of distilling the most important information from source/sources to produce an abridged version for a particular user/users and task/tasks. Automatically generated summaries can significantly reduce the information overload on intelligence analysts in their daily work. Moreover, automated text summarization can be utilized for automated classification and filte...

As the Internet becomes more and more deeply connected with our life, the Internet has brought together mass text material, and it is still in explosive growth. In order to quickly and accurately to help users find the required content, the traditional solution is to use a search engine. However, the results of existing automatic webpage summarization systems for search engine are of low qualit...

This paper presents the LIA summarization systems participating to DUC 2007. This is the second participation of the LIA at DUC and we will discuss our systems in both main and update tasks. The system proposed for the main task is the combination of seven different sentence selection systems. The fusion of the system outputs is made with a weighted graph where the cost functions integrate the ...

Multi Sentence Compression (MSC) is of great value to many real world applications, such as guided microblog summarization, opinion summarization and newswire summarization. Recently, word graph-based approaches have been proposed and become popular in MSC. Their key assumption is that redundancy among a set of related sentences provides a reliable way to generate informative and grammatical se...

News production, delivery, and consumption are increasing in ubiquity and speed, spreading over more software and hardware platforms, in particular mobile devices. This has led to an increasing interest in automated methods for multi-document summarization. We start this chapter with discussing several new alternatives for automated news summarization, with a particular focus on temporal text m...

A graph stream, which refers to the graph with edges being updated sequentially in a form of a stream, has important applications in cyber security and social networks. Due to the sheer volume and highly dynamic nature of graph streams, the practical way of handling them is by summarization. Given a graph stream G, directed or undirected, the problem of graph stream summarization is to summariz...

Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing descriptions of lead to information overload and further cause people confused. this backdrop, automatic summarization received much attenti...

Multi Sentence Compression (MSC) is of great value to many real world applications, such as guided microblog summarization, opinion summarization and newswire summarization. Recently, word graph-based approaches have been proposed and become popular in MSC. Their key assumption is that redundancy among a set of related sentences provides a reliable way to generate informative and grammatical se...

Graph theory is a well studied discipline, and so are the fields of natural language processing and information retrieval. However, most of the times, they are perceived as different disciplines, with different algorithms, different applications, and different potential end-users. The goal of this tutorial is to provide an overview of methods and applications in natural language processing and ...