Jingqiang Chen

Jingqiang Chen

Two crucial issues for text summarization to generate faithful summaries are to make use of knowledge beyond text and to make use of cross-sentence relations in text. Intuitive ways for the two issues are Knowledge Graph (KG) and Graph Neural Network (GNN) respectively. Entities are semantic units in text and in KG. This paper focuses on both issues by leveraging entities mentioned in text to connect GNN and KG for summarization. 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. Secondly, entities are leveraged to link the graph to KG to collect knowledge. Thirdly, entities guide a two-step summarization framework defining a multi-task selector to select salient sentences and entities, and using an entity-focused abstractor to compress the sentences. GNN is connected with KG by constructing sentence-entity graphs where entity-entity edges are built based on KG, initializing entity embeddings on KG, and training entity embeddings using entity-entity edges. The relational heterogeneous GNN utilizes both edge weights and edge types in GNN to calculate graphs with weighted multi-type edges. Experiments show the proposed method outperforms extractive baselines including the HGNN-based HGNNSum and abstractive baselines including the entity-driven SENECA on CNN/DM, and outperforms most baselines on NYT50. Experiments on sub-datasets show the density of sentence-entity edges greatly influences the performance of the proposed method. The greater the density, the better the performance. Ablations show effectiveness of the method.

Jingqiang Chen

Coherent entity-aware multi-image captioning aims to generate coherent captions for neighboring images in a news document. There are coherence relationships among neighboring images because they often describe same entities or events. These relationships are important for entity-aware multi-image captioning, but are neglected in entity-aware single-image captioning. Most existing work focuses on single-image captioning, while multi-image captioning has not been explored before. Hence, this paper proposes a coherent entity-aware multi-image captioning model by making use of coherence relationships. The model consists of a Transformer-based caption generation model and two types of contrastive learning-based coherence mechanisms. The generation model generates the caption by paying attention to the image and the accompanying text. The caption-caption coherence mechanism aims to render entities in the caption of the image be also in captions of neighboring images. The caption-image-text coherence mechanism aims to render entities in the caption of the image be also in the accompanying text. To evaluate coherence between captions, two coherence evaluation metrics are proposed. The new dataset DM800K is constructed that has more images per document than two existing datasets GoodNews and NYT800K, and is more suitable for multi-image captioning. Experiments on three datasets show the proposed captioning model outperforms 7 baselines according to BLUE, Rouge, METEOR, and entity precision and recall scores. 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.

Linpu Jiang, Ke-Jia Chen, Jingqiang Chen

Self-supervised learning on graphs has recently drawn a lot of attention due to its independence from labels and its robustness in representation. Current studies on this topic mainly use static information such as graph structures but cannot well capture dynamic information such as timestamps of edges. Realistic graphs are often dynamic, which means the interaction between nodes occurs at a specific time. This paper proposes a self-supervised dynamic graph representation learning framework (DySubC), which defines a temporal subgraph contrastive learning task to simultaneously learn the structural and evolutional features of a dynamic graph. 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. The subgraph representation function is then designed according to the influence of neighborhood nodes on the central node after encoding the nodes in each subgraph. Finally, the structural and temporal contrastive loss are defined to maximize the mutual information between node representation and temporal subgraph representation. Experiments on five real-world datasets demonstrate that (1) DySubC performs better than the related baselines including two graph contrastive learning models and four dynamic graph representation learning models in the downstream link prediction task, and (2) the use of temporal information can not only sample more effective subgraphs, but also learn better representation by temporal contrastive loss.