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Found 9 papers, 5 papers with code
Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization
Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications.
Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction
Existing methods are limited by their neglect of the multiple entity pairs in one sentence sharing very similar contextual information (ie, the same text and image), resulting in increased difficulty in the MMRE task.
FedCQA: Answering Complex Queries on Multi-Source Knowledge Graphs via Federated Learning
An entity can be involved in various knowledge graphs and reasoning on multiple KGs and answering complex queries on multi-source KGs is important in discovering knowledge cross graphs.
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders
Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization.
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies.
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks.
Attend and select: A segment selective transformer for microblog hashtag generation
These tokens or phrases may originate from primary fragmental textual pieces (e. g., segments) in the original text and are separated into different segments.
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline Summarization
We meticulously analyze the corpus using well-known metrics, focusing on the style of the summaries and the complexity of the summarization task.
Noised Consistency Training for Text Summarization
In this paper, we argue that this limitation can be overcome by a semi-supervised approach: consistency training which is to leverage large amounts of unlabeled data to improve the performance of supervised learning over a small corpus.
