|Title: ||Automatic multidocument summarization of research abstracts: Design and user evaluation|
|Citation: ||Journal of the American Society for Information Science and Technology, 58 (10): 1419-1435|
|Journal: ||Journal of the American Society for Information Science and Technology|
|Issue Date: ||2007 |
|Additional Links: ||http://www3.interscience.wiley.com/journal/114278630/abstract?CRETRY=1&SRETRY=0|
|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.|
Natural language processing
|Appears in Collections: ||Computational Linguistics Group|
Computational Linguistics Group
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