There are a lot of opinionated documents such as blogs, reviews and forum articles on the Web. Because of the huge volume of opinionated documents, automatic summarization techniques have been studied. Some previous opinion summarization techniques focus on predicting sentiment orientation or finding ratings of aspects. For example, to understand opinions about a currently available tablet computer “TabletXYZ”, articles, blogs and reviews from websites can be reviewed. Aspects about the tablet computer, such as “OS (operating system)”, battery, screen, and price can be used to classify the sentiment orientation of the associated text. Although existing techniques can show the general opinion distribution (e.g. 70% positive and 30% negative), they may not provide detailed reasons about those opinions. Thus, reviewing all of the classified texts may still be required.
In some cases, automatic summarization techniques can be helpful to reduce the length of the text of the opinionated document. However, because many automatic summarization techniques are based on “popularity” (frequently mentioned information is important), the output summary can be a repeat of already known information. For example, for the summary request for “positive opinion about TabletXYZ OS”, the output summary might be “OS is good.” Such an output summary is redundant with the initial summary request and does not provide any additional information.
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