Ontology-based Automatic Summarization of Web Document

Zhu, Jun; Jiang, Yi; Li, Bin; Sun, Mingwei

doi:10.4156/ijact.vol4.issue14.34

Cited by 3 publications

(1 citation statement)

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“…The sentence selection process is usually based on linguistic, mathematical and statistical techniques. The extractive summary may not be coherent, but it delivers the main idea about the input text [10]. In contrast, abstractive text summarization techniques attempt to build an internal semantic representation of the original text and then create a summary closer a human-generated one.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

et al. 2018

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The fast-growing amount of information on the Internet makes the research in automatic document summarization very urgent. It is an effective solution for information overload. Many approaches have been proposed based on different strategies, such as latent semantic analysis (LSA). However, LSA, when applied to document summarization, has some limitations which diminish its performance. In this work, we try to overcome these limitations by applying statistic and linear algebraic approaches combined with syntactic and semantic processing of text. First, the part of speech tagger is utilized to reduce the dimension of LSA. Then, the weight of the term in four adjacent sentences is added to the weighting schemes while calculating the input matrix to take into account the word order and the syntactic relations. In addition, a new LSA-based sentence selection algorithm is proposed, in which the term description is combined with sentence description for each topic which in turn makes the generated summary more informative and diverse. To ensure the effectiveness of the proposed LSA-based sentence selection algorithm, extensive experiment on Arabic and English are done. Four datasets are used to evaluate the new model, Linguistic Data Consortium (LDC) Arabic Newswire-a corpus, Essex Arabic Summaries Corpus (EASC), DUC2002, and Multilingual MSS 2015 dataset. Experimental results on the four datasets show the effectiveness of the proposed model on Arabic and English datasets. It performs comprehensively better compared to the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Latent Semantic Analysis Approach for Arabic Document Summarization

et al. 2018

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Design and Implementation of E-Commerce Recommendation System Based on Ontology Technology

Wang

Zhang

2014

AMR

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Electronic commerce recommendation system can effectively retain customers, effective means to improve the electronic commerce system sales. This paper first analyzes the E-commerce recommender system based on ontology, and applies it to the clothing e-commerce website customer relationship management and personalized commodity recommendation; semantic structure through ontology has to commodity recommendation. The paper presents design and implementation of E-commerce recommendation system based on ontology technology so as to effectively improve customer satisfaction.

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