Multi-document Summarization Using Link Analysis Based on Rhetorical Relations between Sentences

Zahri, Nik Adilah Hanin; Fukumoto, Fumiyo

doi:10.1007/978-3-642-19437-5_27

Cited by 11 publications

(10 citation statements)

References 9 publications

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“…Zahri and Fukumoto (2011) determined the CST relations by applying SVM classifier. The PageRank algorithm was used for sentence weighting whereby the directionality in PageRank was determined using the identified CST relations.…”

Section: Discourse Based Methodsmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

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Section: Discourse Based Methodsmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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“…Reference [7] used boosting, a classification algorithm to identify the presence of CST Change of perspective relationships in sentence pairs. Closely related to our work is the approach by [8]. The authors determined five types of CST relation between sentences using SVMs.…”

Section: Subsumptionmentioning

confidence: 99%

Automatic identification of cross-document structural relationships

Kumar

Salim

Hamza

et al. 2012

2012 International Conference on Information Retrieval &Amp; Knowledge Management

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Analysis on inter-document relationship is one of the important studies in multi document analysis. In this paper, we will focus on some special properties that multi document articles hold, specifically news articles. Information across news articles reporting on the same story are often related. Cross-document Structure Theory (CST) gives the relationship between pairs of sentences from different documents. For example, two sentences might have relationships such as identical, overlapping or contradicting. Our aim here is to automatically identify some of these CST relationships. We applied the well known machine learning technique, SVMs for this purpose and obtained some comparable results.

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“…They utilised CST in search for topically related Portuguese documents. They applied a supervised approach based on similarity measures calculated for sentence pairs from different documents: Z a h r i and F u k u m o t o [37] applied the supervised learning to identify a limited set of CST relations: Identity, Paraphrase, Subsumption, Elaboration and Partial Overlap. They were used in the multi-document summarization task.…”

Section: Related Workmentioning

confidence: 99%

“…The features of A l e i x o and P a r d o [1] were expanded with: 1) cosine similarity of word vectors, 2) intersection of common words measured with the Jaccard Index, 3) an indicator of longer sentence (1 if S1 was longer, 0 if equal, -1 if S1 was shorter), 4) and uni-directional word coverage ratio (S1 → S2 and S2 → S1). K u m a r et al [12] followed Z a h r i and F u k u m o t o [37], but restricted the set of relations further down to four: Identity, Subsumption, Overlap and Elaboration.…”

Section: Related Workmentioning

confidence: 99%

“…For the same relations K u m a r, S a l i m and R a z a [13] presented results obtained with SVM, a Feed-Forward neural network and CBR (Case-based Reasoning). The features of Z a h r i and F u k u m o t o [37] were extended with the Jaccard based similarity of noun phrases and verb phrases from the compared sentences. The best result was achieved with CBR based on the cosine similarity measure.…”

Section: Related Workmentioning

confidence: 99%

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Graph-Based Complex Representation in Inter-Sentence Relation Recognition in Polish Texts

Janz

Kędzia

Piasecki

2018

Cybernetics and Information Technologies

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This paper presents a supervised approach to the recognition of Crossdocument Structure Theory (CST) relations in Polish texts. Its core is a graphbased representation constructed for sentences. Graphs are built on the basis of lexicalised syntactic-semantic relations extracted from text. Similarity between sentences is calculated as similarity between their graphs, and the values are used as features to train the classifiers. Several different configurations of graphs, as well as graph similarity methods were analysed for this task. The approach was evaluated on a large open corpus annotated manually with 17 types of selected CST relations. The configuration of experiments was similar to those known from SEMEVAL and we obtained very promising results.

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Multi-document Summarization Using Link Analysis Based on Rhetorical Relations between Sentences

Cited by 11 publications

References 9 publications

A Review on Automatic Text Summarization Approaches

A Review on Automatic Text Summarization Approaches

Automatic identification of cross-document structural relationships

Graph-Based Complex Representation in Inter-Sentence Relation Recognition in Polish Texts

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