Concentric network symmetry grasps authors' styles in word adjacency networks

Amancio, Diego R.; Silva, Filipi N.; Costa, Luciano da Fontoura

doi:10.1209/0295-5075/110/68001

Cited by 48 publications

(48 citation statements)

References 29 publications

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“…• Symmetry (S): the network symmetry is a normalized version of accessibility, where the number of accessible nodes is used as normalization factor [34]. The symmetry uses the concept of concentric level (h) of a node i (see Figure 3), which is defined as the set of nodes h hops away from i.…”

Section: Network Measurementsmentioning

confidence: 99%

Extractive multi-document summarization using multilayer networks

Tohalino

Amancio

2018

Physica A: Statistical Mechanics and its Applications

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Huge volumes of textual information has been produced every single day. In order to organize and understand such large datasets, in recent years, summarization techniques have become popular. These techniques aims at finding relevant, concise and non-redundant content from such a big data. While network methods have been adopted to model texts in some scenarios, a systematic evaluation of multilayer network models in the multi-document summarization task has been limited to a few studies. Here, we evaluate the performance of a multilayer-based method to select the most relevant sentences in the context of an extractive multi document summarization (MDS) task. In the adopted model, nodes represent sentences and edges are created based on the number of shared words between sentences. Differently from previous studies in multidocument summarization, we make a distinction between edges linking sentences from different documents (inter-layer) and those connecting sentences from the same document (intra-layer). As a proof of principle, our results reveal that such a discrimination between intra-and inter-layer in a multilayered representation is able to improve the quality of the generated summaries. This piece of information could be used to improve current statistical methods and related textual models.it involves paraphrasing sections of the source document and, for this reason, it requires natural language generation tools. In addition, abstractive methods may reuse clauses or phrases from original documents [4].In this work, we target our analysis on extractive summarization applied to a set of documents (MDS).The most traditional employed methods to select relevant sentences for extractive summarization are divided into the following major classes: methods based on word frequency, sentence clustering and machine learning. In recent years, a new class of methods based on network theory have been proposed to analyze texts. Applications of network models in text analysis include the study of scientific documents [5,6,7,8], stylometry [9,10,11,12], sense discrimination [13,14] and several other applications [15,16]. The problem of creating single-document extractive summaries has benefited from these previous network models of texts [17,18,19]. It has been claimed that network features overcome other traditional statistical methods when they are used to identify the most central sentences [17,18]. While most of the studies applying network concepts in summarization have been limited to the single-document counterpart, here we evaluate the usefulness of networks in the multi-document scenario. In particular, the main objective of this paper is to probe whether a discrimination between intra-and inter-layer edges is able to improve the characterization of documents modeled as multilayer complex networks. This is an important feature to be considered in the models because a sentence connected to many other sentences from other documents may indicate a high relevance of the approached topics.In the adopted method, nodes ...

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Section: Network Measurementsmentioning

confidence: 99%

Extractive multi-document summarization using multilayer networks

Tohalino

Amancio

2018

Physica A: Statistical Mechanics and its Applications

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“…One possibility is to map texts into a word adjacency network (WAN), which links adjacent words [18,19]. Amancio et al [20] study the symmetry of word adjacency networks and find that specific authors prefer particular types of symmetric motifs. Based on this, the researchers carry out a text classification of authorship of books in a data set comprising books written by 8 authors.…”

Section: Text Representationmentioning

confidence: 99%

Fusing Logical Relationship Information of Text in Neural Network for Text Classification

Wang

Zeng

2020

Mathematical Problems in Engineering

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With the development of computer science and information science, text classification technology has been greatly developed and its application scenarios have been widened. In traditional process of text classification, the existing method will lose much logical relationship information of text. The logical relationship information of a text refers to the relationship information among different logical parts of the text, such as title, abstract, and body. When human beings are reading, they will take title as an important part to remind the central idea of the article, abstract as a brief summary of the content of the article, and body as a detailed description of the article. In most of the text classification studies, researchers concern more about the relationship among words (word frequency, semantics, etc.) and neglect the logical relationship information of text. It will lose information about the relationship among different parts (title, body, etc.) and have an influence on the performance of text classification. Therefore, we propose a text classification algorithm—fusing the logical relationship information of text in neural network (FLRIOTINN), which complements the logical relationship information into text classification algorithms. Experiments show that the effect of FLRIOTINN is better than the conventional backpropagation neural networks which does not consider the logical relationship information of text.

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“…This operation allows the calculation of transition probabilities considering walks of all lengths between any pair of vertices. This measurement has been employed with sucess in other text classification tasks [28]. • Symmetry: The network symmetry is a normalized version of accessibility, where the number of accessible nodes is used as normalization factor [28].…”

Section: ) Concentric Node Degree: Number Of Edges Extendingmentioning

confidence: 99%

“…This measurement has been employed with sucess in other text classification tasks [28]. • Symmetry: The network symmetry is a normalized version of accessibility, where the number of accessible nodes is used as normalization factor [28]. To calculate this measure, concentric random walks are used as a way to avoid transitions to previous concentric levels.…”

Section: ) Concentric Node Degree: Number Of Edges Extendingmentioning

confidence: 99%

Extractive Multi-document Summarization Using Dynamical Measurements of Complex Networks

Tohalino¹,

Amancio²

2017

2017 Brazilian Conference on Intelligent Systems (BRACIS)

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Due to the large amount of textual information available on Internet, it is of paramount relevance to use techniques that find relevant and concise content. A typical task devoted to the identification of informative sentences in documents is the so called extractive document summarization task. In this paper, we use complex network concepts to devise an extractive Multi Document Summarization (MDS) method, which extracts the most central sentences from several textual sources. In the proposed model, texts are represented as networks, where nodes represent sentences and the edges are established based on the number of shared words. Differently from previous works, the identification of relevant terms is guided by the characterization of nodes via dynamical measurements of complex networks, including symmetry, accessibility and absorption time. The evaluation of the proposed system revealed that excellent results were obtained with particular dynamical measurements, including those based on the exploration of networks via random walks.

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Concentric network symmetry grasps authors' styles in word adjacency networks

Cited by 48 publications

References 29 publications

Extractive multi-document summarization using multilayer networks

Extractive multi-document summarization using multilayer networks

Fusing Logical Relationship Information of Text in Neural Network for Text Classification

Extractive Multi-document Summarization Using Dynamical Measurements of Complex Networks

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