Fuji Ren

TL;DR: This work proposes a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentenceclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries.

TL;DR: This work proposes an approach to address the problem of improving content selection in automatic text summarization by using some statistical tools, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentenceclusion of name entity, sentence inclusion of numerical data, sentence relative length and aggregated similarity.

TL;DR: An up-to-date survey on the sink mobility issue is presented and several representative solutions are described following the proposed taxonomy, to help readers comprehend the development flow within a category.

TL;DR: This work uses blogs as object and data source for Chinese emotional expression analysis, and based on this model, a relatively fine-grained annotation scheme is proposed for manual annotation of an emotion corpus.

TL;DR: The experimental results show that the proposed class-indexing-based TF.IDF.ICS"@dFterm weighting approach is promising over the compared well-known baseline term weighting approaches.