Shashi Narayan, Ronald Cardenas, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata, Jiangsheng Yu, Yi Chang. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

Automatic summarization, a difficult but pressing problem in natural language processing, aims at shortening source documents while retaining main information. In recent years, more statistical machine learning methods have been applied to automatic summarization. In this paper, we propose a novel approach for summarization, based on hierarchical Bayesian model of topic-semantic indexing (TSI) and extraction strategy of average log-likelihood. The new method is tested on Brown corpus, and its performance is analyzed by a well-designed blind experiment of one-way ANOVA on human reviews. The experimental results show that TSI model is promising on topic-driven summarization.

The Gaussian process (GP) approaches to classification synthesize Bayesian methods and kernel techniques, which are developed for the purpose of small sample analysis. Here we propose a GP model and investigate it for the facial expression recognition in the Japanese female facial expression dataset. By the strategy of leave-one-out cross validation, the accuracy of the GP classifiers reaches 93.43% without any feature selection/extraction. Even when tested on all expressions of any particular expressor, the GP classifier trained by the other samples outperforms some frequently used classifiers significantly. In order to survey the robustness of this novel method, the random trial of 10-fold cross validations is repeated many times to provide an overview of recognition rates. The experimental results demonstrate a promising performance of this application.