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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Extractive multi-document summarization using multilayer networks

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Author(s):
Tohalino, Jorge V. [1] ; Amancio, Diego R. [1, 2]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[2] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47408 - USA
Total Affiliations: 2
Document type: Journal article
Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 503, p. 526-539, AUG 1 2018.
Web of Science Citations: 3
Abstract

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 multi-document 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. (C) 2018 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 17/13464-6 - Modelling citation and information graphs: a complex network approach
Grantee:Diego Raphael Amancio
Support Opportunities: Scholarships abroad - Research
FAPESP's process: 16/19069-9 - Using semantical information to classify texts modelled as complex networks
Grantee:Diego Raphael Amancio
Support Opportunities: Regular Research Grants