Conference proceeding
A Relevance Feedback Model for Fractal Summarization
Digital Libraries: International Collaboration and Cross-Fertilization, v 3334, pp 368-377
2004
Abstract
As a result of the recent information explosion, there is an increasing demand for automatic summarization, and human abstractors often synthesize summaries that are based on sentences that have been extracted by machine. However, the quality of machine-generated summaries is not high. As a special application of information retrieval systems, the precision of automatic summarization can be improved by user relevance feedback, in which the human abstractor can direct the sentence extraction process and useful information can be retrieved efficiently. Automatic summarization with relevance feedback is a helpful tool to assist professional abstractors in generating summaries, and in this work we propose a relevance feedback model for fractal summarization. The results of the experiment show that relevance feedback effectively improves the performance of automatic fractal summarization.
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Details
- Title
- A Relevance Feedback Model for Fractal Summarization
- Creators
- Fu Lee Wang - City University of Hong KongChristopher C Yang - Drexel University, Information Science (Informatics)
- Publication Details
- Digital Libraries: International Collaboration and Cross-Fertilization, v 3334, pp 368-377
- Conference
- International Conference on Asia Digital Libraries (Shanghai, China, 13 Dec 2004–17 Dec 2004)
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000226741400040
- Scopus ID
- 2-s2.0-35048816119
- Other Identifier
- 991021861112904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Theory & Methods