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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References
Barnsley, M.F., Jacquin, A.E.: Application of Recurrent Iterated Function Systems to Images. In: Proc. SPIE Visual Comm. and Image Processing 1988, vol. 1001, pp. 122–131 (1988)
Baxendale, P.: Machine-Made Index for Technical Literature - An Experiment. IBM Journal, 354–361 (October) (1958)
Cowie, J., Mahesh, K., Nirenburg, S., Zajaz, R.: MINDS-Multilingual Interactive Document Summarization. In: Working Notes of the AAAI Spring Symposium on Intelligent Text Summarization, California, USA, pp. 131–132 (1998)
Cox, D., et al.: Analysis of Binary Data., 2nd edn. Chapman & Hall, Boca Raton (1988)
Craven, T.C.: Human Creation of Abstracts with Selected Computer Assistance Tools. Information Research 3(4), 4 (1998)
Craven, T.C.: Abstracts Produced Using Computer Assistance. J. of the American Soc. for Info. Sci. 51(8), 745–756 (2000)
Edmundson, H.P.: New Method in Automatic Extraction. J. ACM 16(2), 264–285 (1968)
Endres-Niggemeyer, B., Maier, E., Sigel, A.: How to Implement a Naturalistic Model of Abstracting: Four Core Working Steps of an Expert Abstractor. Information Processing and Management 31(5), 631–674 (1995)
Glaser, B.G., Strauss, A.L.: The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine de Gruyter, New York (1967)
Goldstein, J., et al.: Summarizing Text Documents: Sentence Selection and Evaluation Metrics. In: Proc. SIGIR 1999, pp. 121–128 (1999)
Hearst, M.: Subtopic Structuring for Full-Length Document Access. In: Proc. SIGIR 1993, pp. 56–68 (1993)
Jacquin, A.E.: Fractal Image Coding: a Review. Proc. IEEE 81(10), 1451–1465 (1993)
Kendall, M., Gibbons, J.D.: Rank Correlation Methods, 5th edn. Edward Arnold, New York (1990)
Koike, H.: Fractal Views: A Fractal-Based Method for Controlling Information Display. ACM Tran. on Information Systems, ACM 13(3), 305–323 (1995)
Kupiec, J., et al.: A Trainable Document Summarizer. In: Proc. SIGIR 1995, Seattle, USA (1995)
Lam-Adesina, M., Jones, G.J.F.: Applying Summarization Techniques for Term Selection in Relevance Feedback. In: Proc. SIGIR 2001, pp. 1–9 (2001)
Lin, Y., Hovy, E.H.: Identifying Topics by Position. In: Proc. of Applied Natural Language Processing Conference (ANLP-1997), Washington, DC, pp. 283–290 (1997)
Luhn, H.P.: The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development, 159–165 (1958)
Mandelbrot, B.: The Fractal Geometry of Nature. W.H. Freeman, New York (1983)
Morris, G., Kasper, G.M., Adams, D.A.: The Effect and Limitation of Automated Text Condensing on Reading Comprehension Performance. Info. Sys. Research, 17–35 (1992)
Ogden, W., Cowie, J., Davis, M., Ludovik, E., Molina-Salgado, H., Shin, H.: Getting Information from Documents You Cannot Read: an Interactive Cross-Language Text Retrieval and Summarization System. In: Joint ACM DL/SIGIR Workshop on Multilingual Information Discovery and Access (1999)
Rocchio, J.: Relevance Feedback in Information Retrieval. In: The Smart Retrieval System, pp. 313–323. Prentice Hall, Englewood Cliffs (1971)
Salton, G., Buckley, C.: Term-Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24, 513–523 (1988)
Salton, G., et al.: Improving Retrieval Performance by Relevance Feedback. J. America Soc. for Info. Sci. 41, 288–297 (1990)
Teufel, S., Moens, M.: Sentence Extraction as a Classification Task. In: Workshop of Intelligent and Scalable Text Summarization, ACL/EACL (1997)
Tsujimoto, S., Asada, H.: Understanding Multi-articled Documents. In: Proc. of the 10th Int. Conf. on Pattern Recognition, Atlantic City, N.J, pp. 551–556 (1990)
Wang, F.L., Yang, C.C.: Automatic Summarization of Chinese and English Parallel Documents. In: Proc. 6th Int. Conf. on Asian Digital Libraries, Kuala Lumpur (2003)
Yang, C.C., Li, K.W.: Automatic Construction of English/Chinese Parallel Corpora. J. of American Soc. for Info. Sci. and Tech. 54(8), 730–742 (2003)
Yang, C.C., Wang, F.L.: Fractal Summarization for Mobile Device to Access Large Documents on the Web. In: Proc. 12th Int. WWW Conf, Budapest, Hungary (2003)
Yang, C.C., Wang, F.L.: Fractal Summarization: Summarization Based on Fractal Theory. In: Proc. SIGIR 2003, Toronto, Canada (2003)
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Wang, F.L., Yang, C.C. (2004). A Relevance Feedback Model for Fractal Summarization. In: Chen, Z., Chen, H., Miao, Q., Fu, Y., Fox, E., Lim, Ep. (eds) Digital Libraries: International Collaboration and Cross-Fertilization. ICADL 2004. Lecture Notes in Computer Science, vol 3334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30544-6_40
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DOI: https://doi.org/10.1007/978-3-540-30544-6_40
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