[1] Aone, C., Okurowski, M. E., Gorlinsky, J., & Larsen, B. (1999). A trainable summarizer with knowledge acquired from robust NLP techniques. Mani, I., &
Maybury, M. (eds.), Advances in automated text summarization. Cambridge, Mass.: MIT Press.
[2] Azzam, S., Humphreys, K., & Gaizauskas, R. (1999). Using coreference chains for text summarization. In Proceedings of the ACL’99 Workshop on Coreference and Its Application, Baltimore.
[3] Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval.
Harlow, UK: Addison-Wesley Longman Co Inc.
[4] Barzilay, R., & Elhadad, M. (1997). Using lexical chains for text summarization.
In Proceedings of the ACL/EACL’97 Workshop on Intelligent Scalable Text Summarization, Madrid, Spain (pp. 10-17).
[5] Barzilay, R., McKeown, K. R., & Elhadad, M. (1999). Information fusion in the context of multi-document summarization. In Proceedings of the 37th Conference on Association for Computational Linguistics (ACL’99), College Park, Maryland, MD, USA (pp. 550-557).
[6] Bellegarda, J. R., Butzberger, J. W., & Chow, Y. L. (1996). A novel word clustering algorithm based on latent semantic analysis. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1 (pp. 172-175).
[7] Boros, E., Kantor, P. B., & Neu, D. J. (2001). A clustering based approach to create multi-document summaries. In Proceedings of the Document Understanding Conference (DUC-2001), New Orleans, LSA, USA.
[8] Carbonell, J., & Goldstein, J. (1999). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’98), Melbourne, Australia (pp.
335-336).
[9] Chen, C. H., Basu, K., & Ng, T. (1994). An algorithmic approach to concept exploration in a large knowledge network. Technical report, MIS Department, University of Arizona, Tucson, AZ, USA.
[10] Chen, H. H., & Lin, C. J. (2000). A multilingual news summarizer. In Proceedings of the 17th Conference on Computational Linguistics, Saarbrücken, Germany (pp. 159-165).
[11] Columbia Newsblaster: summarizing all the news on the web. Available at http://www1.cs.columbia.edu/nlp/newsblaster.
[12] Daniel, N., Radev, D. R., & Allison, T. (2003). Sub-event based multi-document summarization. In Proceedings of the HLT-NAACL-03 Text Summarization Workshop, Edmonton, Alberta, Canada (pp. 9-16).
[13] Daumé III, H., Echihabi, A., Marcu, D., Munteanu, D. S. & Soricut, R. (2002).
GLEANS : a generator of logical extracts and abstracts for nice summaries. In Proceedings of the Document Understanding Conference (DUC-2002), Philadelphia, PA, USA.
[14] DeJong, G. F. (1979). Skimming stories in real time: an experiment in integrated understanding. Doctoral dissertation, Computer Science Department, Yale University, New Haven, CT, USA.
[15] Document Understanding Conference (DUC). Available at http://duc.nist.gov.
[16] Edmundson, H. P. (1969). New methods in automatic extracting. Journal of the ACM (JACM), 16(2), 264-285.
[17] Elhadad, M. (1993). Using argumentation to control lexical choice: a functional unification implementation. Ph.D. Thesis, Department of Computer Science, Columbia University, New York, NY, USA.
[18] Eskin, E., Klavans, J., & Hatzivassiloglou, V. (1999). Detecting similarity by applying learning over indicators. In Proceedings of the 37th Conference on Association for Computational Linguistics (ACL’99), College Park, Maryland, MD, USA.
[19] Goldstein, J., Mittal, V., Carbonell, J., & Kantrowitz, M. (2000). Multi-document summarization by sentence extraction. In Proceedings the ANLP/NAACL Workshop on Automatic Summarization, Seattle, WA (pp. 40-48).
[20] Gong, Y., & Liu, X. (2001). Generic text summarization using relevance measure and latent semantic analysis. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’01), New Orleans, LA, USA (pp. 19-25).
[21] Harabagiu, S. M., & Lacatusu, F. (2002). Generating single and multi-document summaries with GISTEXTER. In Proceedings of the Document Understanding Conference (DUC-2002), Philadelphia, PA, USA.
[22] Harabagiu, S. M., & Maiorano, S. (2000). Acquisition of linguistic patterns for knowledge-based information extraction. In Proceedings of the 2nd International Conference on Language Resources and Evaluation (LREC-2000), Athens, Greece.
[23] Hardy, H., Shimizu, N., Strzaliowski, T., Ting, L., & Zhang, X. (2001).
Cross-document summarization by concept classification. In Proceedings of the Document Understanding Conference (DUC-2001), New Orleans, LSA, USA.
[24] Hovy, E., & Lin, C. Y. (1999). Automated text summarization in SUMMARIST.
Mani, I., & Maybury, M. (eds.), Advances in automated text summarization.
Cambridge, Mass.: MIT Press.
[25] Karamuftuoglu, M. (2002). An approach to summarization based on lexical bonds. In Proceedings of the Document Understanding Conference (DUC-2002), Philadelphia, PA, USA.
[26] Kim, J. H., Kim, J. H., & Hwang, D. (2000). Korean text summarization using an aggregate similarity. In Proceedings of the 5th International ACM Workshop on Information Retrieval with Asian Languages, Hong Kong, China (pp. 111-118).
[27] Kupiec, J., Pedersen, J., & Chen, F. (1995). A trainable document summarizer. In Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’95), Seattle, WA, USA (pp. 68-73).
[28] Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25, 259-284.
[29] Lenci, A., Bartolini, R., Calzolari, N., Agua, A., Busemann, S., Cartier, E., Chevreau, K., & Coch, J. (2002). Multilingual summarization by integrating linguistic resources in the MLIS-MUSI project. In Proceedings of the 3rd International Conference on Language Resources and Evaluation (LREC’02), Las Palmas, Canary Island, Spain.
[30] Lin, C. Y. (1999). Training a selection function for extraction. In Proceedings of the 8th International Conference on Information and Knowledge Management (CIKM’99), Kansas City, MO, USA (pp. 55-62).
[31] Lin, C. Y., & Hovy, E. (2002). NeATS in DUC 2002. In Proceedings of the Document Understanding Conference (DUC-2002), Philadelphia, PA, USA.
[32] Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2), 159-165.
[33] Mani, I., & Bloedorn, E. (1999). Summarizing similarities and differences among related documents. Information Retrieval, 1(1-2), 35-67.
[34] Mani, I., & Maybury, M. (eds.) (1999). Advances in automated text summarization. Cambridge, Mass.: MIT Press.
[35] Maosong, S., Dayang, S., & Tsou, B. K. (1998). Chinese word segmentation without using lexicon and hardcrafted training data. In Proceedings of the 17th International Conference on Computational Linguistics (COLING’98) and the 36th Annual Meeting of the Association for Computational Linguistics (ACL’98) (COLING-ACL’98), Montreal, Quebec, Canada (pp. 1265-1271).
[36] McKeown, K. R., Evans, D., Nekova, A., Barzilay, R., Hatzivassiloglou, V., Schiffman, B., Blair-Goldensohn, S., Klavans, J., & Sigelman, S. (2002). The Columbia Multi-document Summarizer for DUC 2002. In Proceedings of the
Document Understanding Conference (DUC-2002), Philadelphia, PA, USA.
[37] McKeown, K. R., Klavans, J. L., Hatzivassiloglou, V., Barzilay, R., & Eskin, E.
(1999). Towards multidocument summarization by reformulation: progress and prospects. In Proceedings of the 16th National Conference on Artificial Intelligence (AAAI’99), Orlando, FA, USA (pp. 453-460).
[38] MEAD. Available at http://tangra.si.umich.edu/clair/mead.
[39] Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990).
Introduction to WordNet: a on-line lexical database. Lexicography, 3(4), 235-312.
[40] Myaeng, S. H., & Jang, D. (1999). Development and evaluation of a statistically based document system. Mani, I., & Maybury, M. (eds.), Advances in automated text summarization. Cambridge, Mass.: MIT Press.
[41] NewsInEssence: Interactive Multi-source News Summarization. Available at.
http://www.newsinessence.com/nie.cgi.
[42] Otterbacher, J. C., Radev, D. R., & Luo, A. (2002). Revisions that improve cohesion in multi-document summaries: a preliminary study. In Proceedings of the ACL-02 Workshop on Automatic Summarization, Philadelphia, PA, USA (pp.
27-36).
[43] PERSIVAL: Personalized Search and Summarization over Multimedia Information. Available at http://persival.cs.columbia.edu.
[44] Radev, D. R., Fan, W., & Zhang, Z. (2001). WebInEssence: a personalized web-based multi-document summarization and recommendation system. In Proceedings of the NAACL Workshop on Automatic Summarization, Pittsburgh, PA.
[45] Radev, D. R., Jing, H., & Budzikowska, M. (2000). Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. In Proceedings of the ANLP/NAACL Workshop on Automatic Summarization, Seattle, WA (pp. 21-30).
[46] Radev, D. R., & McKeown, K. R. (1998). Generating natural language summaries from multiple on-line sources. Computational Linguistic, 24(3), 469-500.
[47] Robin, J. (1994). Revision-based generation of natural language summaries providing historical background: corpus-based analysis, design, implementation and evaluation. Ph.D. Thesis, Department of Computer Science, Columbia University, New York, NY, USA.
[48] Salton, G., Singhal, A., Mitra, M., & Buckley, C. (1997). Automatic text structuring and summarization. Information Processing & Management, 33(2), 193-207.
[49] Schiffman, B., Mani, I., & Concepcion, K. J. (2001). Producing biographical summaries: combing linguistic knowledge with corpus statistics. In Proceedings of European Association for Computational Linguistics.
[50] TIPSTER Text Summarization Evaluation Conference (SUMMAC). Available at http://www.itl.nist.gov/iaui/894.02/related_projects/tipster_summac.
[51] Wacholder, N. (1998). Simplex NPs clustered by head: a method for identifying significant topics in a document. In Proceedings of Workshop on the Computational Treatment of Nominals, COLING-ACL, Montreal, Canada (pp.
70-79).
[52] Yeh, J. Y., Ke, H. R., & Yang, W. P. (2004). Text summarization using a trainable summarizer and latent semantic analysis. Information Processing & Management:
Special Issue on ICADL 2002. Accepted and to be appeared. (Recommended by ICADL 2002).
[53] Young, S. R., & Hayes, P. J. (1985). Automatic classification and summarization of banking telexes. In Proceedings of the 2nd Conference on Artificial Intelligence Applications (pp. 402-408).
[54] Zhang, Z., Blair-Goldensohn, S., & Radev, D. R. (2002). Towards CST-enhanced summarization. In Proceedings of the 18th National Conference on Artificial Intelligence (AAAI’02), Edmonton, Alberta, Canada (pp. 439-445).
[55] 陳鈺瑾 (2000). 可調式之中文文件自動摘要. 碩士論文, 國立清華大學資訊 工程研究所, 新竹, 台灣.
[56] 黃聖傑 (1999). 多文件自動摘要方法研究. 碩士論文, 國立台灣大學資訊工 程研究所, 台北, 台灣.
[57] 蘇哲君 (2001). 中英雙語多文件自動摘要系統研究. 碩士論文, 國立台灣大 學資訊工程研究所, 台北, 台灣.
[58] Lin, C-Y. 2004. ROUGE: a Package for Automatic Evaluation of Summaries.
Proc. of the Workshop on the Text Summarization Branches Out (WAS), Barcelona, Spain, 2004.