Compact explanatory opinion summarization

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2013, Pages 1697-1702

  Kim, Hyun Duk ^a   Castellanos, Malu ^b   Hsu, Meichun ^b   Zhai, Cheng Xiang ^a   Dayal, Umeshwar ^b   Ghosh, Riddhiman ^b  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

Compact explanatory summarization; Explanatory phrase extraction; Opinion mining

Indexed keywords

COMPACT EXPLANATORY SUMMARIZATION; EVALUATION MEASURES; GENERAL METHOD; OPINION MINING; PHRASE EXTRACTION; TEXT SEGMENTS; TEXT SUMMARIZATION;

HIDDEN MARKOV MODELS;

KNOWLEDGE MANAGEMENT;

EID: 84889577295     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2505515.2505596     Document Type: Conference Paper

References (19)

1. L. E. Baum, T. Petrie, G. Soules, and N. Weiss. A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. The Annals of Mathematical Statistics, 41(1):164-171, 1970.
2. J. Carbonell and J. Goldstein. 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, pages 335-336, New York, NY, USA, 1998. ACM.
3. T. Cohn and M. Lapata. Sentence compression beyond word deletion. In Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1, COLING '08, pages 137-144, Stroudsburg, PA, USA, 2008. Association for Computational Linguistics.
4. G. Erkan and D. R. Radev. Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res., 22(1):457-479, 2004.
5. K. Ganesan, C. Zhai, and J. Han. Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In Proceedings of the 23rd International Conference on Computational Linguistics, COLING '10, pages 340-348, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.
6. M. Hu and B. Liu. Mining and summarizing customer reviews. In KDD '04: Proceedings of the tenth ACM SIGKDD, pages 168-177, New York, NY, USA, 2004. ACM.
7. J. Jiang and C. Zhai. Extraction of coherent relevant passages using hidden markov models. ACM Trans. Inf. Syst., 24(3):295-319, July 2006.
8. H. D. Kim, M. G. Castellanos, M. Hsu, C. Zhai, U. Dayal, and R. Ghosh. Ranking explanatory sentences for opinion summarization. In Proceedings of the 36th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '13, New York, NY, USA, 2013. ACM.
9. H. D. Kim and C. Zhai. Generating comparative summaries of contradictory opinions in text. In CIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management, pages 385-394, New York, NY, USA, 2009. ACM.
10. D. Klein and C. D. Manning. Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, ACL '03, pages 423-430, Stroudsburg, PA, USA, 2003. Association for Computational Linguistics.
11. B. Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.
12. B. Liu. Sentiment analysis and subjectivity. In N. Indurkhya and F. J. Damerau, editors, Handbook of Natural Language Processing, Second Edition. CRC Press, Taylor and Francis Group, Boca Raton, FL, 2010. ISBN 978-1420085921.
13. Y. Lu, C. Zhai, and N. Sundaresan. Rated aspect summarization of short comments. In WWW '09: Proceedings of the 18th international conference on World wide web, pages 131-140, New York, NY, USA, 2009. ACM.
14. Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai. Topic sentiment mixture: modeling facets and opinions in weblogs. In WWW '07: Proceedings of the 16th international conference on World Wide Web, pages 171-180, New York, NY, USA, 2007. ACM.
15. B. Pang and L. Lee. Opinion mining and sentiment analysis. Found. Trends Inf. Retr., 2(1-2):1-135, 2008.
16. A.-M. Popescu and O. Etzioni. Extracting product features and opinions from reviews. In HLT-EMNLP'05, pages 339-346, Morristown, NJ, USA, 2005. Association for Computational Linguistics.
17. L. R. Rabiner. Readings in speech recognition. chapter A tutorial on hidden Markov models and selected applications in speech recognition, pages 267-296. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1990.
18. S. Verberne. Developing an approach for why-question answering. In Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, EACL '06, pages 39-46, Stroudsburg, PA, USA, 2006. Association for Computational Linguistics.
19. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst., 22(2):179-214, Apr. 2004.