A Multi-document Summarization System for Sociology Dissertation Abstracts: Design, Implementation and Evaluation

Ou, Shiyan; Khoo, Christopher S. G.; Goh, Dion Hoe‐Lian

doi:10.1007/11551362_40

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The decision tree has also been used to summarize multiple documents. Ou et al used decision trees to design their multi-document summarization system [17]. They proposed a multi-document summarization system for sociology dissertation abstracts.…”

Section: -1-machine Learning-based Approachesmentioning

confidence: 99%

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Afsharizadeh¹,

Ebrhimpour-Komeleh²,

Bagheri³

et al. 2022

jist

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Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations.

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Section: -1-machine Learning-based Approachesmentioning

confidence: 99%

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Afsharizadeh¹,

Ebrhimpour-Komeleh²,

Bagheri³

et al. 2022

jist

View full text Add to dashboard Cite

show abstract

“…Typically, the summarization systems consider a collection of documents as a set of individual documents with flatstructure. Given a set of documents, some summarization systems extract concepts and their relationships, and then integrate the extracted information as a summary [15] [16]. Alternatively, some systems segment the documents into some small text units.…”

Section: Hierarchical Summarization For Multiple Documentsmentioning

confidence: 99%

“…Nowadays, the tfidf (Term Frequency, Inverse Document Frequency) method is the most widely used method to calculate the thematic weight of keywords [15]. • It is believed that the topic sentences tend to occur at the beginning or the end of documents or paragraphs [1].…”

Section: Hierarchical Summarization Modelmentioning

confidence: 99%

Organization of Documents for Multiple Document Summarization

Wang

Wong

Mak

2008

2008 Seventh International Conference on Web-Based Learning

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Multiple document summarization technique can be utilized for improving the effectiveness of retrieval and accessibility of learning objects in e-learning. Related study has shown that hierarchical summarization is a promising technique which can effectively extract the most important information from the source documents. Hierarchical summarization has been extended to summarization of multiple documents. Three hierarchical structures were proposed to organize a set of related documents. This paper investigates the impact of document structure on hierarchical summarization.

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“…The summarization method was evaluated at two levels: (a) accuracy and usefulness of each important summarization step and (b) overall quality and usefulness of the summaries. The evaluation of each summarization step has been reported by Ou et al (2005b). This article focused on the second level.…”

Section: Review Of Multidocument Summarization and Evaluation Approachesmentioning

confidence: 99%

“…Technical details of the summarization method have been detailed elsewhere (e.g., Ou, Khoo, & Goh, 2005b). This article focuses on reporting the user evaluation, involving 20 sociological researchers and 40 general users, which was carried out to assess the overall quality and usefulness of the summaries.…”

Section: Introductionmentioning

confidence: 99%

Automatic multidocument summarization of research abstracts: Design and user evaluation

Khoo

Goh

2007

J. Am. Soc. Inf. Sci.

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The purpose of this study was to develop a method for automatic construction of multidocument summaries of sets of research abstracts that may be retrieved by a digital library or search engine in response to a user query. Sociology dissertation abstracts were selected as the sample domain in this study. A variable-based framework was proposed for integrating and organizing research concepts and relationships as well as research methods and contextual relations extracted from different dissertation abstracts. Based on the framework, a new summarization method was developed, which parses the discourse structure of abstracts, extracts research concepts and relationships, integrates the information across different abstracts, and organizes and presents them in a Web-based interface. The focus of this article is on the user evaluation that was performed to assess the overall quality and usefulness of the summaries. Two types of variable-based summaries generated using the summarization method - with or without the use of a taxonomy - were compared against a sentence-based summary that lists only the research-objective sentences extracted from each abstract and another sentence-based summary generated using the MEAD system that extracts important sentences. The evaluation results indicate that the majority of sociological researchers (70%) and general users (64%) preferred the variable-based summaries generated with the use of the taxonomy

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A Multi-document Summarization System for Sociology Dissertation Abstracts: Design, Implementation and Evaluation

Cited by 6 publications

References 6 publications

A Survey on Multi-document Summarization and Domain-Oriented Approaches

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Organization of Documents for Multiple Document Summarization

Automatic multidocument summarization of research abstracts: Design and user evaluation

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