Document (#31783)

Author

Ou, S.
Khoo, C.S.G.
Goh, D.H.

Title

Multi-document summarization of news articles using an event-based framework

Source

Aslib proceedings. 58(2006) no.3, S.276-291

Year

2006

Abstract

Purpose - The purpose of this research is to develop a method for automatic construction of multi-document summaries of sets of news articles that might be retrieved by a web search engine in response to a user query. Design/methodology/approach - Based on the cross-document discourse analysis, an event-based framework is proposed for integrating and organizing information extracted from different news articles. It has a hierarchical structure in which the summarized information is presented at the top level and more detailed information given at the lower levels. A tree-view interface was implemented for displaying a multi-document summary based on the framework. A preliminary user evaluation was performed by comparing the framework-based summaries against the sentence-based summaries. Findings - In a small evaluation, all the human subjects preferred the framework-based summaries to the sentence-based summaries. It indicates that the event-based framework is an effective way to summarize a set of news articles reporting an event or a series of relevant events. Research limitations/implications - Limited to event-based news articles only, not applicable to news critiques and other kinds of news articles. A summarization system based on the event-based framework is being implemented. Practical implications - Multi-document summarization of news articles can adopt the proposed event-based framework. Originality/value - An event-based framework for summarizing sets of news articles was developed and evaluated using a tree-view interface for displaying such summaries.

Theme

Automatisches Abstracting

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4. Kar, M.; Nunes, S.; Ribeiro, C.: Summarization of changes in dynamic text collections using Latent Dirichlet Allocation model (2015) 0.27

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5. Xiong, S.; Ji, D.: Query-focused multi-document summarization using hypergraph-based ranking (2016) 0.24

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