Guided Structure-Aware Review Summarization

【摘要】 Although the goal of traditional text summarization is to generate summaries with diverse information,most of those applications have no explicit definition of the information structure.Thus,it is difficult to generate truly structureaware summaries because the information structure to guide summarization is unclear.In this paper,we present a novel framework to generate guided summaries for product reviews.The guided summary has an explicitly defined structure which comes from the important aspects of products.The proposed framework attempts to maximize expected aspect satisfaction during summary generation.The importance of an aspect to a generated summary is modeled using Labeled Latent Dirichlet Allocation.Empirical experimental results on consumer reviews of cars show the effectiveness of our method.更多还原

【Abstract】 Although the goal of traditional text summarization is to generate summaries with diverse information,most of those applications have no explicit definition of the information structure.Thus,it is difficult to generate truly structureaware summaries because the information structure to guide summarization is unclear.In this paper,we present a novel framework to generate guided summaries for product reviews.The guided summary has an explicitly defined structure which comes from the important aspects of products.The proposed framework attempts to maximize expected aspect satisfaction during summary generation.The importance of an aspect to a generated summary is modeled using Labeled Latent Dirichlet Allocation.Empirical experimental results on consumer reviews of cars show the effectiveness of our method.更多还原