A complex network approach to text summarization

Antiqueira, Lucas; Oliveira, Osvaldo N.; Costa, Luciano da Fontoura; Nunes, Maria das Graças Volpe

doi:10.1016/j.ins.2008.10.032

Cited by 120 publications

(109 citation statements)

References 50 publications

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“…Moreover, edge relations can further combine two or more different statistical, linguistic or other criteria, for instance in syntactic-semantic association graphs (Nastase et al 2006;Widdows and Dorow 2002). Edge relations can also be further refined, for instance in co-occurrence graphs which define co-occurrence either within a fixed window (Ferrer i and Solé 2001;Masucci and Rodgers 2006;Milo et al 2004), or within the same sentence (Antiqueira et al 2009;Caldeira et al 2005). Optionally, meaningless edge-relations can be filtered out, under any statistical or linguistic interpretation of this (Pado and Lapata 2007).…”

Section: Text As Graphmentioning

confidence: 99%

Graph-based term weighting for information retrieval

Blanco

Lioma²

2011

Inf Retrieval

169

142

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A standard approach to Information Retrieval (IR) is to model text as a bag of words. Alternatively, text can be modelled as a graph, whose vertices represent words, and whose edges represent relations between the words, defined on the basis of any meaningful statistical or linguistic relation. Given such a text graph, graph theoretic computations can be applied to measure various properties of the graph, and hence of the text. This work explores the usefulness of such graph-based text representations for IR. Specifically, we propose a principled graph-theoretic approach of (1) computing term weights and (2) integrating discourse aspects into retrieval. Given a text graph, whose vertices denote terms linked by co-occurrence and grammatical modification, we use graph ranking computations (e.g. PageRank Page et al. in The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998) to derive weights for each vertex, i.e. term weights, which we use to rank documents against queries. We reason that our graph-based term weights do not necessarily need to be normalised by document length (unlike existing term weights) because they are already scaled by their graph-ranking computation. This is a departure from existing IR ranking functions, and we experimentally show that it performs comparably to a tuned ranking baseline, such as BM25 (Robertson et al. in NIST Special Publication 500-236: TREC-4, 1995). In addition, we integrate into ranking graph properties, such as the average path length, or clustering coefficient, which represent different aspects of the topology of the graph, and by extension of the document represented as a graph. Integrating such properties into ranking allows us to consider issues such as discourse coherence, flow and density during retrieval. We experimentally show that this type of ranking performs comparably to BM25, and can even outperform it, across different TREC (Voorhees and Harman in TREC: Experiment and evaluation in information retrieval, MIT Press, 2005) datasets and evaluation measures.

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Section: Text As Graphmentioning

confidence: 99%

Graph-based term weighting for information retrieval

Blanco

Lioma²

2011

Inf Retrieval

169

142

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“…In computer science, complex networks have been used to understand the topology and dynamics of the Internet [36], characterization of social networks [20], text summarization [2], aspects of scientific co-authorship [29], and citation networks [32].…”

Section: Related Work On Complex Networkmentioning

confidence: 99%

A complex network approach for nanoparticle agglomeration analysis in nanoscale images

et al. 2017

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Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is due to the characteristics of nanoparticles images and due to their multiple intricate properties; one property of recurrent interest is the agglomeration of particles. Addressing this issue, this paper introduces an approach that uses complex networks to detect and describe nanoparticle agglomerates so to foster easier and more insightful analyses. In this approach, each detected particle in an image corresponds to a vertice and the distances between the particles define a criterion for creating edges. Edges are created if the distance is smaller than a radius of interest. Once this network is set, we calculate several discrete measures able to reveal the most outstanding agglomerates in a nanoparticle image. Experimental results using images of Scanning Tunneling Microscopy (STM) of gold nanoparticles demonstrated the effectiveness of the pro- The results also demonstrated efficacy for both convex and non-convex agglomerates.

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“…Graphs have shown to be applicable to many Natural Language Processing applications [13] and there are several graph-based approaches for both single and multi-document summarization (see, for example, [3,8,9,14,15,16,17,18,19]). …”

Section: Text Summarizationmentioning

confidence: 99%

“…Antiqueira et al [17,18] use complex networks to model texts for single document summarization. In their networks/graphs, each sentence is represented as a node and links are established among sentences that share at least one noun.…”

Section: Text Summarizationmentioning

confidence: 99%

Exploring the subtopic-based relationship map strategy for multi-document summarization

Ribaldo

Cardoso²,

Pardo³

2016

RITA

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Neste artigo, foi adaptada e explorada uma estratégia de geração de sumários multidocumento com base na abordagem de mapas de relacionamento, a qual representa textos como grafos (mapas) de segmentos inter-relacionados e aplica técnicas de percurso em grafo para produzir os sumários. Em particular, utilizou-se o Caminho Denso-Segmentado, um método sofisticado que tenta representar em um sumário os principais subtópicos dos textos de origem, mantendo a informatividade do sumário. Além disso, também foram investigadas algumas técnicas de segmentação e agrupamento de subtópicos bem conhecidas, a fim de selecionar corretamente as informações mais relevantes para compor o sumário final. Mostra-se que este método de sumarização baseado em subtópicos supera outros métodos de sumarização multidocumento e atinge resultados do estado da arte, competindo com os métodos de sumarização profundos mais sofisticados da área. Explorando mapas de relacionamento com base em subtópicos para sumarização multidocumento Abstract: In this paper we adapt and explore a strategy for generating multidocument summaries based on the relationship map approach, which represent texts as graphs (maps of interrelated segments and apply traversing techniques for producing the summaries. In particular, we work on the Segmented Bushy Path, a sophisticated method which tries to represent in a summary the main subtopics from the source texts while keeping its informativeness. In addition, we also investigate some well-known subtopic segmentation and clustering techniques in order to correctly select the most relevant information to compose the final summary. We show that this subtopic-based summarization method outperforms other methods for multi-document summarization and achieves state of the art results, competing with the most sophisticated deep summarization methods in the area.

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A complex network approach to text summarization

Cited by 120 publications

References 50 publications

Graph-based term weighting for information retrieval

Graph-based term weighting for information retrieval

A complex network approach for nanoparticle agglomeration analysis in nanoscale images

Exploring the subtopic-based relationship map strategy for multi-document summarization

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