Combinations of Different Raters for Text Summarization
نویسنده
چکیده
Because of the unprecedented growth of information on the web it became difficult to decide what information is truly relevant. We present a method of personalized summarization based on a combination of different raters that process the additional information, such as user characteristics or document metadata to extract from the document information that is important for a particular user or in a chosen domain, thus decreasing the information overload. We have experimented with the proposed method in the adaptive web-based educational system ALEF. Our results suggest that user-added annotations can be used to improve the summaries’ quality compared to the generic variant.
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