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Found 9 papers, 1 papers with code
Neural Abstractive Multi-Document Summarization: Hierarchical or Flat Structure?
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.
Gradable ChatGPT Translation Evaluation
ChatGPT, as a language model based on large-scale pre-training, has exerted a profound influence on the domain of machine translation.
Parallel Hierarchical Transformer with Attention Alignment for Abstractive Multi-Document Summarization
In comparison to single-document summarization, abstractive Multi-Document Summarization (MDS) brings challenges on the representation and coverage of its lengthy and linked sources.
TS-BERT: A fusion model for Pre-trainning Time Series-Text Representations
Use one of the news text and the stock data as the primary information source for the prediction task and the other as the auxiliary information source.
Are Crises Predictable? A Review of the Early Warning Systems in Currency and Stock Markets
The study efforts to explore and extend the crisis predictability by synthetically reviewing and comparing a full mixture of early warning models into two constitutions: crisis identifications and predictive models.
Global-aware Beam Search for Neural Abstractive Summarization
A global scoring mechanism is then developed to regulate beam search to generate summaries in a near-global optimal fashion.
Integrated Node Encoder for Labelled Textual Networks
Voluminous works have been implemented to exploit content-enhanced network embedding models, with little focus on the labelled information of nodes.
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions.
News2vec: News Network Embedding with Subnode Information
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.
