Abstract
This paper discusses fundamental issues involved in word selection for title generation. We review several methods for title generation, namely extractive summarization and two versions of a Naïve Bayesian, and compare the performance of those methods using an F1 metric. In addition, we introduce a novel approach to title generation using the k-nearest neighbor (KNN) algorithm. Both the KNN method and a limited-vocabulary Naïve Bayesian method outperform the other evaluated methods with an F1 score of around 20%. Since KNN produces complete and legible titles, we conclude that KNN is a very promising method for title generation, provided good content overlap exists between the training corpus and the test documents.
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References
M. Witbrock and V. Mittal: Ultra-Summarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries. In Proceedings of SIGIR 99, Berkeley, CA, August 1999
J. Kupiec, J. Pedersen, and F. Chen: A trainable document summarizer. In Proceedings of ACM/SIGIR.95, pages 68–73. ACM
J. Goldstein, M. Kantrowitz, V. Mittal, and J. Carbonell: Summarizing Text Documents: Sentence Selection and Evaluation Metrics. In Proceedings of SIGIR 99, Berkeley, CA, August 1999.
T. Strzalkowski, J. Wang, and B. Wise: A robust practical text summarization system. In AAAI Intelligent Text Summarization Workshop, pp. 26–30, Stanford, CA, March, 1998.
G. Salton, A. Singhal, M. Mitra, and C. Buckley: Automatic text structuring and summary. Info. Proc. And Management, 33(2):193–207, March, 1997.
M. Mitra, A. Sighal, and C. Buckley: Automatic text summarization by paragraph extraction. In Proceedings of the ACL.97/EACL.97 Workshop on Intelligent Scalable Text Summarization, Madrid, Spain.
Y. Yang, C. G. Chute: An example-based mapping method for text classification and retrieval. ACM Transactions on Information Systems (TOIS), 12(3):252–77. 1994.
V. Rjiesbergen: Information Retrieval. Chapter 7.. Butterworths, London, 1979.
G. Salton (ed): The SMART Retrieval System: Experiments in Automatic Document Proceeding. Prentice Hall, Englewood Cliffs, New Jersey. 1971.
V. Mittal, M. Kantrowitz, J. Goldstein and J. Carbonell: Selecting Text Spans for Document Summaries: Heuristics and Metrics. AAAI-99.
H. Nye: The Use of a One Stage Dynamic Programming Algorithm for Connected Word Recognition. IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. AASP-32, No 2, pp. 262–271, April 1984.
P. Kennedy and A.G. Hauptmann: Automatic Title Generation for the Informedia Multimedia Digital Library. ACM Digital Libraries, DL-2000, San Antonio Texas, May 2000, in press.
Primary Source Media, Broadcast News CDROM, Woodbridge, CT, 1997
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Jin, R., Hauptmann, A.G. (2001). Title Generation Using a Training Corpus. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2001. Lecture Notes in Computer Science, vol 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44686-9_23
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DOI: https://doi.org/10.1007/3-540-44686-9_23
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