Heterogeneous Graph Neural Networks for Extractive Document Summarization: A Detailed Evaluation
The paper "Heterogeneous Graph Neural Networks for Extractive Document Summarization," authored by Danqing Wang et al., presents a novel approach to extractive document summarization through the implementation of heterogeneous graph neural networks, termed HeterSUMGraph. The work addresses key challenges related to modeling cross-sentence relations, particularly in the contexts of both single-document and multi-document summarization.
Methodology Overview
The core innovation of this paper lies in leveraging a heterogeneous graph network structure that includes nodes of different semantic granularity. Specifically, the network involves sentence nodes and word nodes, allowing for an enriched and robust interaction model between sentences within a document. This methodology diverges from traditional approaches that rely on homogeneous node structures by incorporating semantic units like words to act as intermediaries within the network, enhancing the capture of inter-sentence relationships.
The authors utilize a Graph Attention Network (GAT) to pass messages between node types iteratively, thereby improving the feature representations of the nodes based on graph topology and semantic relevance. This iterative message-passing mechanism provides a dynamic and fine-grained understanding of sentence importance, which is critical for effective summarization.
For multi-document summarization, the paper extends its framework, introducing document supernodes, which integrate document-level information into the summarization task. This modification allows the proposed methodology to maintain its effectiveness across various document sources, enhancing the adaptability of the approach.
Empirical Evaluation
The authors report extensive evaluations of their proposed method across multiple benchmark datasets, including CNN/DailyMail and NYT50 for single-document summarization, and Multi-News for multi-document summarization. The performance is assessed using standard ROUGE metrics.
- On the CNN/DailyMail dataset, the HeterSUMGraph demonstrates superior performance compared to various baseline models, with significant improvements seen in ROUGE-1, ROUGE-2, and ROUGE-L scores. Notably, the implementation of trigram blocking results in further performance boosts, highlighting the model's efficacy in reducing redundancy in generated summaries.
- For the NYT50 dataset, the results corroborate the effectiveness observed in CNN/DailyMail, though the impact of trigram blocking was less pronounced, attributed to differences in summary composition between datasets.
- In the context of multi-document summarization evaluated on the Multi-News dataset, the HeterDocSUMGraph, leveraging document nodes, outperformed competitive baselines, underscoring the model’s robustness in capturing document-level cross-references and interdependencies.
Contributions and Implications
The paper contributes significantly to the field of graph-based neural frameworks for document summarization by proposing a flexible, scalable, and semantically rich network structure. Noteworthy is the incorporation of edge features in the graph, which utilize TF-IDF values to quantify and leverage the significance of connections between nodes. Such a framework resolves limitations tied to distance-dependent models like RNNs, by ensuring that sentence-node relationships are captured in a contextually relevant manner through shared semantic units (words).
The proposed framework’s flexibility offers promising pathways for future research, particularly in applying pre-trained LLMs like BERT to further improve node representations. Additionally, this work lays a solid foundation for exploring more complex semantic units beyond words, such as entities and discourse relations, to enable even richer graph structures.
Conclusion
Overall, the approach presented by Danqing Wang and colleagues establishes a sophisticated paradigm for document summarization tasks by aligning node representation learning with semantic structures inherent in documents. This initiative expands the utilization of heterogeneous graphs in NLP and suggests a promising trajectory for advances in neural summarization methodologies. The paper illustrates the potential of integrating graph-based techniques within the domain of document summarization, demonstrating a clear avenue for further exploration and refinement.