In this paper we present REG, a graph-based approach for study a fundamental problem of Natural Language Processing (NLP): the automatic text summarization. The algorithm maps a document as a graph, then it computes the weight of their sentences. We have applied this approach to summarize documents in three languages.

Rapid improvement of electronic documents in World Wide Web has made overload to the users in accessing the information. Therefore, abstracting the primary content from numerous documents related to same topic is highly essential. Summarization of multiple documents helps in valuable decision-making in less time. This paper proposed a framework named Adept Multi-Document Summarization (AMDS) fo...

Event-based summarization extracts and organizes summary sentences in terms of the events that the sentences describe. In this work, we focus on semantic relations among event terms. By connecting terms with relations, we build up event term graph, upon which relevant terms are grouped into clusters. We assume that each cluster represents a topic of documents. Then two summarization strategies ...

In-memory visualization and analysis of graphs is hard if they cannot be fit in the memory. For this purpose, creating a summary graph to understand their insights is very useful. In this paper, we present an efficient algorithm for MDL based graph summarization to compress big graphs. We have evaluated the performance of the proposed algorithm on a real graph and observe better execution time ...

We propose a new way of generating personalized single document summary by combining two complementary methods: collaborative filtering for tag recommendation and graph-based affinity propagation. The proposed method, named by Collaborative Summarization, consists of two steps iteratively repeated until convergence. In the first step, the possible tags of one user on a new document are predicte...

Indexing used in text summarization has been an active area of current researches. Text summarization plays a crucial role in information retrieval. Snippets generated by web search engines for each query result is an application of text summarization. Existing text summarization techniques shows that the indexing is done on the basis of the words in the document and consists of an array of the...

Graph-based learning algorithms have been shown to be an effective approach for query-focused multi-document summarization (MDS). In this paper, we extend the standard graph ranking algorithm by proposing a two-layer (i.e. sentence layer and topic layer) graph-based semi-supervised learning approach based on topic modeling techniques. Experimental results on TAC datasets show that by considerin...

Large-scale networks are widely used to represent object relationships in many real world applications. The occurrence of large-scale networks presents significant computational challenges to process, analyze, and extract information from such networks. Network summarization techniques are commonly used to reduce the computational load while attempting to maintain the basic structural propertie...