With regards to WikiSum (CITATION) that empowers applicative explorations of Neural Multi-Document Summarization (MDS) to learn from large scale dataset, this study develops two hierarchical Transformers (HT) that describe both the cross-token and cross-document dependencies, at the same time allow extended length of input documents. By incorporating word- and paragraph-level multi-head attentions in the decoder based on the parallel and vertical architectures, the proposed parallel and vertical hierarchical Transformers (PHT &VHT) generate summaries utilizing context-aware word embeddings together with static and dynamics paragraph embeddings, respectively. A comprehensive evaluation is conducted on WikiSum to compare PHT &VHT with established models and to answer the question whether hierarchical structures offer more promising performances than flat structures in the MDS task. The results suggest that our hierarchical models generate summaries of higher quality by better capturing cross-document relationships, and save more memory spaces in comparison to flat-structure models. Moreover, we recommend PHT given its practical value of higher inference speed and greater memory-saving capacity.

With the development of NLP technologies, news can be automatically categorized and labeled according to a variety of characteristics, at the same time be represented as low dimensional embeddings. However, it lacks a systematic approach that effectively integrates the inherited features and inter-textual knowledge of news to represent the collective information with a dense vector. With the aim of filling this gap, the News2vec model is proposed to allow the distributed representation of news taking into account its associated features. To describe the cross-document linkages between news, a network consisting of news and its attributes is constructed. Moreover, the News2vec model treats the news node as a bag of features by developing the Subnode model. Based on the biased random walk and the skip-gram model, each news feature is mapped to a vector, and the news is thus represented as the sum of its features. This approach offers an easy solution to create embeddings for unseen news nodes based on its attributes. To evaluate our model, dimension reduction plots and correlation heat-maps are created to visualize the news vectors, together with the application of two downstream tasks, the stock movement prediction and news recommendation. By comparing with other established text/sentence embedding models, we show that News2vec achieves state-of-the-art performance on these news-related tasks.