Graph-based Neural Multi-Document Summarization (1706.06681v3)

Abstract: We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use a greedy heuristic to extract salient sentences while avoiding redundancy. In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks. Our model improves upon traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multi-document summarization systems.

Overview of "Graph-based Neural Multi-Document Summarization"

The paper "Graph-based Neural Multi-Document Summarization" explores the enhancement of multi-document summarization (MDS) through the integration of neural networks and graph-based techniques. The authors introduce a novel summarization model that leverages Graph Convolutional Networks (GCNs) applied to sentence relation graphs, improving the selection and extraction of salient sentences from document clusters. This method demonstrates the utilization of graph structures to effectively encapsulate inter-sentence relationships, which presents an advancement over existing extractive and neural-based approaches.

Methodology

The core innovation of this research is the use of GCNs to process sentence embeddings derived from Recurrent Neural Networks (RNNs). By encoding sentence relations in graph representations, the system improves MDS by capturing intricate relationships across sentences. Three graph representation techniques are evaluated:

1. Cosine Similarity Graph: Edges are created based on tf-idf cosine similarity between sentence pairs.
2. Approximate Discourse Graph (ADG): Based on G-Flow models that consider discourse relation indicators.
3. Personalized Discourse Graph (PDG): A novel enhancement that incorporates sentence personalization to augment discourse-based edge weights, improving edge diversity and information.

These graph representations are input into a GCN, which refines sentence embeddings for salience estimation. Sentence salience is calculated using a regression model incorporating node features and global document cluster embeddings. The summarization system then employs a greedy heuristic for sentence extraction, prioritizing salience while minimizing redundancy.

Experimental Results

The authors evaluated their model on the DUC 2004 dataset, benchmarking against traditional and state-of-the-art MDS systems. The results underscore the model's competitive performance, achieving better or comparable ROUGE scores compared to leading systems, notably RegSum. The inclusion of graph structures particularly enhanced the model's ability to discern and represent critical relationships, improving summarization quality over baseline models lacking graph inputs.

Implications and Future Directions

This research underscores the potential of integrating graph-based approaches with deep learning for text summarization tasks. By improving the model's understanding of inter-sentence relations, such techniques can respond more robustly to the complexities of MDS. The use of PDGs highlights the role of incorporating discourse structure and personalization in edge formation, a direction that promises further exploration. Future studies might investigate scaling this approach to larger datasets or incorporating more sophisticated features, such as semantic or contextual information, to further enhance performance. Given the ongoing developments in AI and natural language processing, the approach laid out in this paper offers avenues for deeper integration of structured information in neural models, advancing the state of MDS.