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For decades, research in natural language processing (NLP) has focused on summarization. Sequence-to-sequence models for abstractive summarization have been studied extensively, yet generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, summarizers need to acquire the co-references that form multiple types of relations over input sentences, e.g., 1-to-N, N-to-1, and N-to-N relations, since the structured knowledge for text usually appears on these relations. By allowing the decoder to pay different attention to the input sentences for the same entity at different generation states, the structured graph representations generate more informative summaries. In this paper, we propose a hierarchical graph attention networks (HGATs) for abstractive summarization with a topicsensitive PageRank augmented graph. Specifically, we utilize dual decoders, a sequential sentence decoder, and a graph-structured decoder (which are built hierarchically) to maintain the global context and local characteristics of entities, complementing each other. We further design a greedy heuristic to extract salient users’ comments while avoiding redundancy to drive a model to better capture entity interactions. Our experimental results show that our models produce significantly higher ROUGE scores than variants without graph-based attention on both SSECIF and CNN/Daily Mail (CNN/DM) datasets.
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Gated Graph Neural Networks (GG-NNs) for Abstractive Multi-Comment Summarization
Abstract—Summarization of long sequences into a concise statement is a core problem in natural language processing, which requires a non-trivial understanding of the weakly structured text. Therefore, integrating crowdsourced multiple users’ comments into a concise summary is even harder because (1) it requires transferring the weakly structured comments to structured knowledge. Besides, (2) the users comments are informal and noisy. In order to capture the long-distance relationships in staggered long sentences, we propose a neural multi-comment summarization (MCS) system that incorporates the sentence relationships via graph heuristics that utilize relation knowledge graphs, i.e., sentence relation graphs (SRG) and approximate discourse graphs (ADG). Motivated by the promising results of gated graph neural networks (GG-NNs) on highly structured data, we develop a GG-NNs with sequence encoder that incorporates SRG or ADG in order to capture the sentence relationships. Specifically, we employ the GG-NNs on both relation knowledge graphs, with the sentence embeddings as the input node features and the graph heuristics as the edges’ weights. Through multiple layerwise propagations, the GG-NNs generate the salience for each sentence from high-level hidden sentence features. Consequently, we use a greedy heuristic to extract salient users’ comments while avoiding the noise in comments. The experimental results show that the proposed MCS improves the summarization performance both quantitatively and qualitatively.
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- Award ID(s):
- 1946391
- PAR ID:
- 10321975
- Date Published:
- Journal Name:
- 2021 IEEE International Conference on Big Knowledge (ICBK)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICKG52313.2021.00050
