Title :
STARLET: Multi-document Summarization of Service and Product Reviews with Balanced Rating Distributions
Author :
Fabbrizio, Giuseppe Di ; Aker, Ahmet ; Gaizauskas, Robert
Author_Institution :
Dept. of Comput. Sci., Univ. of Sheffield, Sheffield, UK
Abstract :
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.
Keywords :
information retrieval; reviews; text analysis; STARLET; balanced rating distribution; evaluative text; multidocument summarization; opinion distribution; product review; service review; summarization feature; user reviews; Adaptation models; Data mining; Feature extraction; Measurement; Predictive models; Redundancy; Training; A* search; Summarization; evaluative text; multi-ratings prediction;
Conference_Titel :
Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4673-0005-6
DOI :
10.1109/ICDMW.2011.158
