A document-sensitive graph model for multi-document summarization

In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influen...

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Bibliographic Details
Published in:Knowledge and information systems Vol. 22; no. 2; pp. 245 - 259
Main Authors: Wei, Furu, Li, Wenjie, Lu, Qin, He, Yanxiang
Format: Journal Article
Language:English
Published: London Springer-Verlag 01.02.2010
Springer
Springer Nature B.V
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Artificial intelligence
Computer based modeling
Computer Science
Computer science; control theory; systems
Data Mining and Knowledge Discovery
Database Management
Documents
Electronic documents
Exact sciences and technology
Graph theory
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
Information systems. Data bases
IT in Business
Memory organisation. Data processing
Ratings & rankings
Regular Paper
Software
Speech and sound recognition and synthesis. Linguistics
Studies
Theoretical computing
Query-oriented summarization
Graph-based ranking algorithm
Inter- and intra-document relation
Graph-based summarization model
Generic summarization
Database query
Hierarchical classification
Abstract
Transmission protocol
Iterative method
Graph theory
Modeling
Document structure
Linguistics
Sentence
Document analysis
ISSN:0219-1377, 0219-3116
Online Access:Get full text
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Summary:In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influence of documents on sentences. In this paper, we present a novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation. By exploiting document–document and document–sentence relations, we distinguish intra-document sentence relations from inter-document sentence relations. In such a way, we move towards the goal of truly summarizing multiple documents rather than a single combined document. Based on this model, we develop an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking). Automatic ROUGE evaluations on the DUC data sets show that DsR outperforms previous graph-based models in both generic and query-oriented summarization tasks.
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ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-009-0194-2