As a core task of natural language processing and information retrieval, automatic text summarization is widely applied in many fields. There are two existing methods for text summarization task at present: abstractive and extractive. On this basis we propose a novel hybrid model of extractive-abstractive to combine BERT (Bidirectional Encoder Representations from Transformers) word embedding with reinforcement learning. Firstly, we convert the human-written abstractive summaries to the ground truth labels. Secondly, we use BERT word embedding as text representation and pre-train two sub-models respectively. Finally, the extraction network and the abstraction network are bridged by reinforcement learning. To verify the performance of the model, we compare it with the current popular automatic text summary model on the CNN/Daily Mail dataset, and use the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics as the evaluation method. Extensive experimental results show that the accuracy of the model is improved obviously.

Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis. In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification. Specifically, we first propose a lexicon-enhanced attention mechanism by combining the sentiment lexicon with an attention mechanism to incorporate sentiment linguistic knowledge into deep learning methods. Second, we introduce a multi-head attention mechanism in the deep neural network to interactively capture the contextual information from different representation subspaces at different positions. Furthermore, we stack a LAN model to build a hierarchical sentiment classification model for large-scale text. Extensive experiments are conducted to evaluate the effectiveness of the proposed models on four popular real-world sentiment classification datasets at both the sentence level and the document level. The experimental results demonstrate that our proposed models can achieve comparable or better performance than the state-of-the-art methods.

Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity.