Ontology-enriched multi-document summarization in disaster management

Li, Lei; Wang, Dingding; Shen, Chao; Li, Tao

doi:10.1145/1835449.1835632

Cited by 23 publications

(17 citation statements)

References 5 publications

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“…With the support of the ontology, information can be related with each other through the shared and common understanding of a domain (Khelif et al, 2007). Li et al (2010) developed the Ontology-enriched Multi-Document Summarization (OMS) system to generate query-relevant summary from a collection of documents. OMS first links the sentences from documents onto a domain-related ontology, then maps the given query to a specific node in the ontology and finally extracts the summary from the sentences in the sub-tree rooted at the corresponding query node.…”

Section: Knowledge Based Methodmentioning

confidence: 99%

Automatic Multi Document Summarization Approaches

Kumar

Salim²

2012

Journal of Computer Science

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Problem statement: Text summarization can be of different nature ranging from indicative summary that identifies the topics of the document to informative summary which is meant to represent the concise description of the original document, providing an idea of what the whole content of document is all about. Approach: Single document summary seems to capture both the information well but it has not been the case for multi document summary where the overall comprehensive quality in presenting informative summary often lacks. It is found that most of the existing methods tend to focus on sentence scoring and less consideration is given to the contextual information content in multiple documents. Results: In this study, some survey on multi document summarization approaches has been presented. We will direct our focus notably on four well known approaches to multi document summarization namely the feature based method, cluster based method, graph based method and knowledge based method. The general ideas behind these methods have been described. Conclusion: Besides the general idea and concept, we discuss the benefits and limitations concerning these methods. With the aim of enhancing multi document summarization, specifically news documents, a novel type of approach is outlined to be developed in the future, taking into account the generic components of a news story in order to generate a better summary.

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Section: Knowledge Based Methodmentioning

confidence: 99%

Automatic Multi Document Summarization Approaches

Kumar

Salim²

2012

Journal of Computer Science

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“…In later work, Li et al (2010) proposed Ontologyenriched Multi-Document Summarization (OMS) system to generate query-relevant summary applied to disaster management; for natural calamities related news and reports. OMS relates sentences onto a domain-specific ontology.…”

Section: News Summarizationmentioning

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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“…There are 4 filters including time, resource type, zoom level, and disaster type. Any combination of those filters can be used to give users a visual summary and corresponding textual summary [19,20] of the disaster situation for corresponding query conditions.…”

Section: Disaster Sitrep System Componentsmentioning

confidence: 99%

“…They are designed to provide structurally organized terminologies that are formal, application-independent and with common agreement within a community of practice [9,19]. However, generating taxonomy from the scratch suffers high-cost, low-efficiency problem.…”

Section: Concept Hierarchy Generationmentioning

confidence: 99%