Abstractive text summarization is to generate concise summaries that well preserve both salient information and the overall semantic meanings of the given documents. However, real-world documents, e.g., financial reports, generally contain rich data such as charts and tabular data which invalidates most existing text summarization approaches. This paper is thus motivated to propose this novel approach to simultaneously summarize both textual and tabular data. Particularly, we first manually construct a “table+text → summary” dataset. Then, the tabular data is respectively embedded in a row-wise and column-wise manner, and the textual data is encoded at the sentence-level via an employed pre-trained model. We propose a salient detector gate respectively performed between each pair of row/column and sentence embeddings. The highly correlated content is considered as salient information that must be summarized. Extensive experiments have been performed on our constructed dataset and the promising results demonstrate the effectiveness of the proposed approach w.r.t. a number of both automatic and human evaluation criteria.

Session-based recommendation has recently attracted more and more research efforts. Most existing approaches are intuitively proposed to discover users' potential preferences or interests from the anonymous session data. This apparently ignores the fact that these sequential behavior data usually reflect session user's potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session has become a challenging task. To tackle the aforementioned issue, this paper proposes a novel demand-aware graph neural network model. Particularly, a demand modeling component is designed to extract the underlying multiple demands of each session. Then, the demand-aware graph neural network is designed to first construct session demand graphs and then learn the demand-aware item embeddings to make the recommendation. The mutual information loss is further designed to enhance the quality of the learnt embeddings. Extensive experiments have been performed on two real-world datasets and the proposed model achieves the SOTA model performance.