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Found 6 papers, 2 papers with code
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network
Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations.
Ranked #1 on
Extractive Text Summarization
on CNN / Daily Mail
Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks
Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content.
Deep Structural Point Process for Learning Temporal Interaction Networks
A temporal interaction network consists of a series of chronological interactions between users and items.
Document-level Relation Extraction with Dual-tier Heterogeneous Graph
Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.
HIN: Hierarchical Inference Network for Document-Level Relation Extraction
In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level.
Ranked #51 on
Relation Extraction
on DocRED
