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 classification is a pivotal task in NLP (Natural Language Processing), which has received widespread attention recently. Most of the existing methods leverage the power of deep learning to improve the performance of models. However, these models ignore the interaction information between all the sentences in a text when generating the current text representation, which results in a partial semantics loss. Labels play a central role in text classification. And the attention learned from text-label in the joint space of labels and words is not leveraged, leaving enough room for further improvement. In this paper, we propose a text classification method based on Self-Interaction attention mechanism and label embedding. Firstly, our method introduce BERT (Bidirectional Encoder Representation from Transformers) to extract text features. Then Self-Interaction attention mechanism is employed to obtain text representations containing more comprehensive semantics. Moreover, we focus on the embedding of labels and words in the joint space to achieve the dual-label embedding, which further leverages the attention learned from text-label. Finally, the texts are classified by the classifier according to the weighted labels representations. The experimental results show that our method outperforms other state-of-the-art methods in terms of classification accuracy.

Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks.

Stock prediction is a challenging task due to multiple influencing factors and complex market dependencies. Traditional solutions are based on a single type of information. With the success of multi-source information in different fields, the combination of different types of information such as numerical and textual information has become a promising option. Although multi-source information provides rich multi-view information, how to mine and construct structured relationships from them is a difficult problem. Specifically, most existing methods usually extract features from commonly used multi-source information as predictive information sources, without further pre-constructing stock relationship graphs with dependencies using broader information. More importantly, they typically treat each stock as an isolated forecasting, or employ stock market correlations based on a fixed predefined graph structure, but current methods are not sensitive enough to aggregate the attribute features extracted from multi-source information and stock relationship graph, to obtain the dynamic update of market relations and relationship strength. The stock market is highly temporally, and the attributes of nodes are affected by the time perception of other attributes, which is not fully considered. To address these problems, we propose a novel dynamic attributes-driven graph attention networks incorporating sentiment (DGATS) information, transaction data, and text data. Inspired by behavioral finance, we separately extract sentiment information as a factor of technical indicators, and further realize the early fusion of technical indicators and textual data through Kronecker product-based tensor fusion. In particular, by LSTM and temporal attention network, the short-term and long-term transition features are gradually grasped from the local composition of the fused stock trading sequence. Furthermore, real-time intra-market dependencies and key attributes information are captured with graph networks, enabling dynamic updates of relationships and relationship strengths in predefined graphs. Experiments on the real datasets show that the architecture can outperform the previous methods in prediction performance.

Predicting stock movements is a valuable research field that can help investors earn more profits. As with time‐series data, the stock market is time‐dependent and the value of historical information may decrease over time. Accurate prediction can be achieved by mining valuable information with words on social platforms and further integrating it with actual stock market conditions. However, many methods still cannot effectively dig deep into hidden information, integrate text and stock prices, and ignore the temporal dependence. Therefore, to solve the above problems, we propose a transformer‐based attention network framework that uses historical text and stock prices to capture the temporal dependence of financial data. Among them, the transformer model and attention mechanism are used for feature extraction of financial data, which has fewer applications in the financial field, and effective analysis of key information to achieve an accurate prediction. A large number of experiments have proved the effectiveness of our proposed method. The actual simulation experiment verifies that our model has practical application value.