2.50
Hdl Handle:
http://hdl.handle.net/2436/27898
Title:
Automatic multidocument summarization of research abstracts: Design and user evaluation
Authors:
Ou, Shiyan; Khoo, Christopher S.G.; Goh, Dion H.
Abstract:
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.
Citation:
Journal of the American Society for Information Science and Technology, 58 (10): 1419-1435
Publisher:
Wiley
Journal:
Journal of the American Society for Information Science and Technology
Issue Date:
2007
URI:
http://hdl.handle.net/2436/27898
DOI:
10.1002/asi.20618
Additional Links:
http://www3.interscience.wiley.com/journal/114278630/abstract?CRETRY=1&SRETRY=0
Type:
Article
Language:
en
ISSN:
15322882; 15322890
Appears in Collections:
Computational Linguistics Group; Computational Linguistics Group

Full metadata record

DC FieldValue Language
dc.contributor.authorOu, Shiyan-
dc.contributor.authorKhoo, Christopher S.G.-
dc.contributor.authorGoh, Dion H.-
dc.date.accessioned2008-05-23T16:24:27Z-
dc.date.available2008-05-23T16:24:27Z-
dc.date.issued2007-
dc.identifier.citationJournal of the American Society for Information Science and Technology, 58 (10): 1419-1435en
dc.identifier.issn15322882-
dc.identifier.issn15322890-
dc.identifier.doi10.1002/asi.20618-
dc.identifier.urihttp://hdl.handle.net/2436/27898-
dc.description.abstractThe 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.en
dc.language.isoenen
dc.publisherWileyen
dc.relation.urlhttp://www3.interscience.wiley.com/journal/114278630/abstract?CRETRY=1&SRETRY=0en
dc.subjectDigestsen
dc.subjectAutomatic abstractingen
dc.subjectNatural language processingen
dc.subjectText miningen
dc.subjectResearch abstracts-
dc.titleAutomatic multidocument summarization of research abstracts: Design and user evaluationen
dc.typeArticleen
dc.identifier.journalJournal of the American Society for Information Science and Technologyen
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