| Project/Area Number |
10680375
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Japan Advanced Institute of Science and Technology, Hokuriku |
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Principal Investigator |
OKUMURA Manabu Japan Advanced Institute of Science and Technology, School of Information Science, Associate Professor, 情報科学研究科, 助教授 (60214079)
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| Co-Investigator(Kenkyū-buntansha) |
MOCHIZUKI Hajime Japan Advanced Institute of Science and Technology, School of Information Science, Assistant, 情報科学研究科, 助手 (70313707)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 1999: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 1998: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | Automatic Text Summarization / Lexical Cohesion / Query-biased Summary / Coherence of a Text / 動的要約生成 |
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| Research Abstract |
The recent prevalence of information retrieval engines has created an important application of automatic summarization as display of retrieval results, whereby the user can quickly and accurately judge the relevance of texts returned as a result of a query. Here, a summary that reflects the user's topic of interest(information need) expressed in the query would be more suitable than producing a generic summary. This type of summary is often called a 'query-biased summary'.
In this work, we present a method for producing query-biased summaries using lexical chains. Lexical chains are sequences of words that have lexical cohesion relationship, and tend to indicate fragments of a text that form a semantic unit. Using lexical chains would enable production of more coherent and readable summaries than previous approaches to query-biased summarization.
To evaluate the effectiveness of our method, we adopt a task-based evaluation scheme. The results of our experiments show that query-biased summaries by lexical chains out-perform others in the accuracy of subjects' relevance judgments.
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