Query-oriented update summarization is an emerging summarization task very recently. It brings new challenges to the sentence ranking algorithms that require not only to locate the important and query-relevant information, but also to capture the new information when document collections evolve. In this paper, we propose a novel graph based sentence ranking algorithm, namely PNR, for update sum...

Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word sense disambiguation, ontology construction, sentiment and subjectivity analysis, text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in la...

This work reports on research conducted on the domain of multi-document summarization using background knowledge. The research focuses on summary evaluation and the implementation of a set of generic use tools for NLP tasks and especially for automatic summarization. Within this work we formalize the n-gram graph representation and its use in NLP tasks. We present the use of n-gram graphs for t...

We propose an efficient text summarization technique that involves two basic operations. The first operation involves finding coherent chunks in the document and the second operation involves ranking the text in the individual coherent chunks and picking the sentences that rank above a given threshold. The coherent chunks are formed by exploiting the lexical relationship between adjacent senten...

The paper presents a new analytical algorithm which is able to carry out direct and exact reliability quantification of highly reliable systems with maintenance (both preventive and corrective). A directed acyclic graph is used as a system representation. The algorithm is based on a special new procedure which permits only summarization between two or more nonnegative numbers that can be very d...

The acquiring of sentence similarity has become a crucial step in graph-based multi-document summarization algorithms which have been intensively studied during the past decade. Previous algorithms generally considered sentence-level structure information and semantic similarity separately, which, consequently, had no access to grab similarity information comprehensively. In this paper, we pres...

Text summarization is an important problem, which has numerous applications. This problem has been extensively studied and many approaches have been proposed in the literature for its solution. One of the most challenging problems in the field of text summarization is generating a user-focused summary based on a query. In this paper, we investigate a new approach that tackles this problem and p...

Summarization evaluation has been always a challenge to researchers in the document summarization field. Usually, human involvement is necessary to evaluate the quality of a summary. Here we present a new method for automatic evaluation of text summaries by using document graphs. Data from Document Understanding Conference 2002 (DUC-2002) has been used in the experiment. We propose measuring th...

Extractive multi-document summarization is mostly treated as a sentence ranking problem. Existing graph-based ranking methods for key-sentence extraction usually attempt to compute a global importance score for each sentence under a single relation. Motivated by the fact that both documents and sentences can be presented by a mixture of semantic topics detected by Latent Dirichlet Allocation (L...

The graph stream is defined as rapid edge streams on a huge domain of nodes. Nowadays, play important roles in network traffic, social networks, and cloud troubleshooting. Therefore, various summary structures for are proposed to obtain approximate evaluation results. However, these either sacrifice accuracy guaranteed throughput or compromise memory consumption high precision. In view the limi...