| 研究実績の概要 |
In this fiscal year, we have achieved good results on social context summarization. Our work published in EICIR 2016 is extended and published in the journal. We developed SoRTESum to take the advantages of social information such as document content reflection to extract summary sentences and social messages. SoRTESum was extensively evaluated on two datasets which shows the state of the art performance. Another work is to present a summary model (SoCRFSum), which is formulated as a sequence labeling problem (CRFs), which exploits the support of external information to model sentences and comments. SoCRFSum was validated on a dataset collected from Yahoo News and show interesting results. We also report a summarization method named SoSVMRank, which integrates the social information of a Web document to generate a high-quality summarization.
Beside that, we have developed two corpora for social contextual summarization in both English and Vietnamese.
Along with research on deploying deep learning for text summarization, we would like to adapt our work by exploiting deep learning techniques. We propose a novel method for mapping a concept to a vector representation using Wiktionary sense definition. We have published an interesting work for turning SVM parameters using Genetic program and it is published in an international journal. This method can compare another models and it can help to turn parameters in SVM learning. This can be widely applied for many machine learning tasks including its applications in text summarization.
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