acl acl2010 acl2010-264 acl2010-264-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Josef Steinberger ; Marco Turchi ; Mijail Kabadjov ; Ralf Steinberger ; Nello Cristianini
Abstract: The main focus of this work is to investigate robust ways for generating summaries from summary representations without recurring to simple sentence extraction and aiming at more human-like summaries. This is motivated by empirical evidence from TAC 2009 data showing that human summaries contain on average more and shorter sentences than the system summaries. We report encouraging preliminary results comparable to those attained by participating systems at TAC 2009.
B. Boguraev and C. Kennedy. 1997. Saliencebased content characterisation of text documents. In I. Mani, editor, Proceedings of the Workshop on Intelligent and Scalable Text Summarization at the Annual Joint Meeting of the ACL/EACL, Madrid. P. Brown, S. Della Pietra, V. Della Pietra, and R. Mercer. 1994. The mathematic of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2):263–3 11. J. Clarke and M. Lapata. 2008. Global inference for sentence compression: An integer linear programming approach. Journal ofArtificial Intelligence Research, 31:273–318. G. Erkan and D. Radev. 2004. LexRank: Graph-based centrality as salience in text summarization. Journal of Artificial Intelligence Research (JAIR). Y. Gong and X. Liu. 2002. Generic text summarization using relevance measure and latent semantic analysis. In Proceedings of ACM SIGIR, New Orleans, US. E. Hovy. 2005. Automated text summarization. In Ruslan Mitkov, editor, The Oxford Handbook of Computational Linguistics, pages 583–598. Oxford University Press, Oxford, UK. K. Knight and D. Marcu. 2002. Summarization beyond sentence extraction: A probabilistic approach to sentence compression. 139(1):91–107. Artificial Intelligence, P. Koehn, F. Och, and D. Marcu. 2003. Statistical phrase-based translation. In Proceedings of NAACL ’03, pages 48–54, Morristown, NJ, USA. P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, and E. Herbst. 2007. Moses: Open source toolkit for statistical machine translation. In Proceedings of ACL ’07, demonstration session. J. Kupiec, J. Pedersen, and F. Chen. 1995. A trainable document summarizer. In Proceedings of the ACM SIGIR, pages 68–73, Seattle, Washington. C. Lin and E. Hovy. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of HLT-NAACL, Edmonton, Canada. NIST, editor. 2009. Proceeding of the Text Analysis Conference, Gaithersburg, MD, November. F. Och and H. Ney. 2001 . Discriminative training and maximum entropy models for statistical machine translation. In Proceedings of ACL ’02, pages 295–302, Morristown, NJ, USA. M. Porter. 1980. An algorithm for suffix stripping. Program, 14(3): 130–137. C. Quirk, C. Brockett, and W. Dolan. 2004. Monolingual machine translation for paraphrase generation. In Proceedings of EMNLP, volume 149. Barcelona, Spain. K. Sp¨ arck-Jones. 1999. Automatic summarising: Factors and directions. In I. Mani and M. Maybury, editors, Advances in Automatic Text Summarization. MIT Press. J. Steinberger and K. Je˘ zek. 2009. Update summarization based on novel topic distribution. In Proceedings of the 9th ACM DocEng, Munich, Germany. J. Steinberger, M. Poesio, M. Kabadjov, and K. Je˘ zek. 2007. Two uses of anaphora resolution in summarization. Information Processing and Management, 43(6): 1663–1680. Special Issue on Text Summarisation (Donna Harman, ed.). R. Zens, F. J. Och, and H. Ney. 2002. Phrase-based statistical machine translation. In Proceedings of KI ’02, pages 18–32, London, UK. Springer-Verlag. 386