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Issue No.03 - May-June (2013 vol.28)
pp: 28-37
Giuseppe Di Fabbrizio , University of Sheffield
Ahmet Aker , University of Sheffield
Robert Gaizauskas , University of Sheffield
Product and service reviews are abundantly available online, but selecting relevant information from them involves a significant amount of time. The authors address this problem with Starlet, a novel approach for extracting multidocument summarizations that considers aspect rating distributions and language modeling. These features encourage the inclusion of sentences in the summary that preserve the overall opinion distribution and reflect the reviews' original language.
Predictive models, Feature extraction, Computational linguistics, Computational modeling, Data mining, Natural language processing, Text analysis,reviews summarization; rating prediction models; A* search, Predictive models, Feature extraction, Computational linguistics, Computational modeling, Data mining, Natural language processing, Text analysis
Giuseppe Di Fabbrizio, Ahmet Aker, Robert Gaizauskas, "Summarizing Online Reviews Using Aspect Rating Distributions and Language Modeling", IEEE Intelligent Systems, vol.28, no. 3, pp. 28-37, May-June 2013, doi:10.1109/MIS.2013.36
1. W. Duan, B. Gu, and A.B. Whinston, “Do Online Reviews Matter? An Empirical Investigation of Panel Data,” J. Decision Support Systems, vol. 45, no. 4, 2008, pp. 1007-1016.
2. D. Park, J. Lee, and I. Han, “The Effect of On-line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement,” Int'l J. Electronic Commerce, vol. 11, July 2007, pp. 125-148.
3. B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, vol. 2, no. 1–2, 2008, pp. 1-135.
4. R. McDonald et al., “Structured Models for Fine-to-Coarse Sentiment Analysis,” Proc. Assoc. Computational Linguistics, Assoc. Computational Linguistics (ACL), 2007, pp. 432-439.
5. M. Hu and B. Liu, “Mining and Summarizing Customer Reviews,” Proc. ACM SIGKDD Conf. Knowledge Discovery and Data Mining, ACM, 2004, pp. 168-177.
6. E. Cambria and A. Hussain, Computing: Techniques, Tools, and Applications, Springer, 2012.
7. J. M. Conroy, J. G. Stewart, and J. D. Schlesinger, “CLASSY Query-Based Multi-Document Summarization,” Proc. Document Understanding Conf. Workshop Conf. on Empirical Methods in Natural Language Processing, Nat'l Inst. Standards and Technology, 2005; .
8. K.R. McKeown et al., “Tracking and Summarizing News on a Daily Basis with Columbia's Newsblaster,” Proc. 2nd Int'l Conf. Human Language Technology Research, Morgan Kaufmann, 2002, pp. 280-285.
9. N. Elhadad et al., “Customization in a Unified Framework for Summarizing Medical Literature,” Artificial Intelligence in Medicine, vol. 33, Feb. 2005, pp. 179-198.
10. T. Copeck, N. Japkowicz, and S. Szpakowicz, “Text Summarization as Controlled Search,” Proc. 15th Conf. Canadian Soc. for Computational Studies of Intelligence on Advances in Artificial Intelligence, Springer-Verlag, 2002, pp. 268-280.
11. H. Saggion and G. Lapal, “Generating Indicative-Informative Summaries with Sumum,” Computational Linguistics, vol. 28, Dec. 2002, pp. 497-526.
12. S. Mithun and L. Kosseim, “Summarizing Blog Entries versus News Texts,” Proc. Workshop Events in Emerging Text Types, ACL, 2009, pp. 1-8.
13. A. Aker and R. Gaizauskas, “Generating Image Descriptions Using Dependency Relational Patterns,” Proc. ACL 2010, ACL, 2010, pp. 1250-1258.
14. L. W. Ku, Y. T. Liang, and H. H. Chen, “Opinion Extraction, Summarization and Tracking in News and Blog Corpora,” Proc. AAAI-2006 Spring Symp. Computational Approaches to Analyzing Weblogs, Am. Assoc. for Artificial Intelligence (AAAI), 2006; SS-06-03SS06-03-020.pdf.
15. S. Russell et al., Artificial Intelligence: A Modern Approach, Prentice Hall, 1995.
16. N. Gupta, G. Di Fabbrizio, and P. Haffner, “Capturing the Stars: Predicting Ratings for Service and Product Reviews,” Proc. NAACL HLT 2010 Workshop Semantic Search, Assoc. Computational Linguistics, 2010, pp. 36-43.
17. A.L. Berger, V.J.D. Pietra, and S.A.D. Pietra, “A Maximum Entropy Approach to Natural Language Processing,” Computational Linguistics, vol. 22, Mar. 1996, pp. 39-71.
18. C.-Y. Lin, “ROUGE: A Package for Automatic Evaluation of Summaries,” Proc. ACL Workshop Text Summarization Branches Out, ACL, 2004, pp. 74-81.
19. F. Jelinek, L. Bahl, and R. Mercer, “Design of a Linguistic Statistical Decoder for the Recognition of Continuous Speech,” IEEE Trans. Information Theory, vol. 21, no. 3, 1975, pp. 250-256.
20. A. Mutton et al., “Gleu: Automatic Evaluation of Sentence-Level Fluency,” Proc. 45th Annual Meeting Assoc. Computational Linguistics, ACL, 2007, pp. 344-351.
21. R. Kneser and H. Ney, “Improved Backing-off for m-Gram Language Modeling,” Proc. IEEE Int'l Conf. Acoustics, Speech and Signal Processing, IEEE, 1995, pp. 181-184.
22. S. Kullback and R. A. Leibler, “On Information and Sufficiency,” The Annals of Mathematical Statistics, vol. 22, no. 1, 1951, pp. 79-86.
23. A. Aker, T. Cohn, and R. Gaizauskas, “Multi-Document Summarization Using A* Search and Discriminative Training,” Proc. Conf. Empirical Methods in Natural Language Processing, ACL, 2010, pp. 482-491.
24. F. Och, “Minimum Error Rate Training in Statistical Machine Translation,” Proc. 41st Annual Meeting Assoc. Computational Linguistics, ACL, 2003, pp. 160-167.
25. M.J.D. Powell, “An Efficient Method for Finding the Minimum of a Function of Several Variables without Calculating Derivatives,” The Computer J, vol. 7, no. 2, 1964, pp. 155-162.
26. H. Dang, “Overview of DUC 2005,” Proc. Document Understanding Conf. Workshop at the Human Language Technology Conf./Conf. Empirical Methods in Natural Language Processing, Nat'l Inst. Standards and Technology, 2005; OVERVIEW05.pdf.
27. J. Goldstein et al., “Summarizing Text Documents: Sentence Selection and Evaluation Metrics,” Proc. 22nd Annual Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, ACM, 1999, pp. 121-128.
28. D. Radev et al., “MEAD—A Platform for Multidocument Multilingual Text Summarization,” Conf. Language Resources and Evaluation (LREC), European Languages Resource Assoc., 2004;∼radev/papers lrec-mead04.pdf.
29. E. Cambria et al., Big Social Data Analysis, Taylor and Francis, 2013, ch. 13.
30. I. Mani et al., “The Tipster Summac Text Summarization Evaluation,” Proc. 9th Conf. European Chapter of the Assoc. for Computational Linguistics, ACL, 1999, pp. 77-85.
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