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2011 IEEE 11th International Conference on Data Mining Workshops
STARLET: Multi-document Summarization of Service and Product Reviews with Balanced Rating Distributions
Vancouver, Canada
December 11-December 11
ISBN: 978-0-7695-4409-0
| ASCII Text | x | ||
| Giuseppe Di Fabbrizio, Ahmet Aker, Robert Gaizauskas, "STARLET: Multi-document Summarization of Service and Product Reviews with Balanced Rating Distributions," 2012 IEEE 12th International Conference on Data Mining Workshops, pp. 67-74, 2011 IEEE 11th International Conference on Data Mining Workshops, 2011. | |||
| BibTex | x | ||
| @article{ 10.1109/ICDMW.2011.158, author = {Giuseppe Di Fabbrizio and Ahmet Aker and Robert Gaizauskas}, title = {STARLET: Multi-document Summarization of Service and Product Reviews with Balanced Rating Distributions}, journal ={2012 IEEE 12th International Conference on Data Mining Workshops}, volume = {0}, year = {2011}, isbn = {978-0-7695-4409-0}, pages = {67-74}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDMW.2011.158}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE 12th International Conference on Data Mining Workshops TI - STARLET: Multi-document Summarization of Service and Product Reviews with Balanced Rating Distributions SN - 978-0-7695-4409-0 SP67 EP74 A1 - Giuseppe Di Fabbrizio, A1 - Ahmet Aker, A1 - Robert Gaizauskas, PY - 2011 KW - Summarization KW - evaluative text KW - A* search KW - multi-ratings prediction VL - 0 JA - 2012 IEEE 12th International Conference on Data Mining Workshops ER - | |||
Reviews about products and services are abundantly available online. However, selecting information relevant to a potential buyer involves a significant amount of time reading user's reviews and weeding out comments unrelated to the important aspects of the reviewed entity. In this work, we present STARLET, a novel approach to multi-document summarization for evaluative text that considers the rating distribution as summarization feature to consistently preserve the overall opinion distribution expressed in the original reviews. We demonstrate how this method improves traditional summarization techniques and leads to more readable summaries.
Index Terms:
Summarization, evaluative text, A* search, multi-ratings prediction
Citation:
Giuseppe Di Fabbrizio, Ahmet Aker, Robert Gaizauskas, "STARLET: Multi-document Summarization of Service and Product Reviews with Balanced Rating Distributions," icdmw, pp.67-74, 2011 IEEE 11th International Conference on Data Mining Workshops, 2011
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