Affiliation:
1. The University of Arizona, Tucson, AZ
Abstract
The use of text summaries in information-seeking research has focused on query-based summaries. Extracting content that resembles the query alone, however, ignores the greater context of the document. Such context may be central to the purpose and meaning of the document. We developed a generic, a query-based, and a hybrid summarizer, each with differing amounts of document context. The generic summarizer used a blend of discourse information and information obtained through traditional surface-level analysis. The query-based summarizer used only query-term information, and the hybrid summarizer used some discourse information along with query-term information. The validity of the generic summarizer was shown through an intrinsic evaluation using a well-established corpus of human-generated summaries. All three summarizers were then compared in an information-seeking experiment involving 297 subjects. Results from the information-seeking experiment showed that the generic summaries outperformed all others in the browse tasks, while the query-based and hybrid summaries outperformed the generic summary in the search tasks. Thus, the document context of generic summaries helped users browse, while such context was not helpful in search tasks. Such results are interesting given that generic summaries have not been studied in search tasks and the that majority of Internet search engines rely solely on query-based summaries.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Reference54 articles.
1. Aone C. Okurowski M. E. Gorlinsky J. and Larsen B. 1999. A trainable summarizer with knowledge acquired from robust NLP techniques. In Advances in Automatic Text Summarization I. Mani and M. T. Maybury Eds. MIT Press Cambridge MA 71--80. Aone C. Okurowski M. E. Gorlinsky J. and Larsen B. 1999. A trainable summarizer with knowledge acquired from robust NLP techniques. In Advances in Automatic Text Summarization I. Mani and M. T. Maybury Eds. MIT Press Cambridge MA 71--80.
2. Barzilay R. and Elhadad M. 1999. Using lexical chains for text summarization. In Advances in Automatic Text Summarization I. Mani and M. T. Maybury Eds. MIT Press Cambridge MA. Barzilay R. and Elhadad M. 1999. Using lexical chains for text summarization. In Advances in Automatic Text Summarization I. Mani and M. T. Maybury Eds. MIT Press Cambridge MA.
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