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Found 6 papers, 2 papers with code
Comparative Graph-based Summarization of Scientific Papers Guided by Comparative Citations
And then, we propose the comparative graph-based summarization (CGSUM) method to create comparative summaries using citations as guidance.
An entity-guided text summarization framework with relational heterogeneous graph neural network
Firstly, entities are leveraged to construct a sentence-entity graph with weighted multi-type edges to model sentence relations, and a relational heterogeneous GNN for summarization is proposed to calculate node encodings.
Transform, Contrast and Tell: Coherent Entity-Aware Multi-Image Captioning
Experiments also show that the generated captions are more coherent than that of baselines according to caption entity scores, caption Rouge scores, the two proposed coherence evaluation metrics, and human evaluations.
GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation
Sequential recommendation based on multi-interest framework models the user's recent interaction sequence into multiple different interest vectors, since a single low-dimensional vector cannot fully represent the diversity of user interests.
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast
Specifically, a novel temporal subgraph sampling strategy is firstly proposed, which takes each node of the dynamic graph as the central node and uses both neighborhood structures and edge timestamps to sample the corresponding temporal subgraph.
Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN
Recent neural summarization research shows the strength of the Encoder-Decoder model in text summarization.
