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Deletion-based Korean Sentence Compression using Graph Neural Networks
Gyoung-Ho Lee, Yo-Han Park, Kong Joo Lee
http://doi.org/10.5626/JOK.2022.49.1.32
Automatic sentence compression aims at generating a concise sentence from a lengthy source sentence. Most common approaches to sentence compression is deletion-based compression. In this paper, we implement deletion-based sentence compression systems based on a binary classifier and long short-term memory (LSTM) networks with attention layers. The binary classifier, which is a baseline model, classifies words in a sentence into words that need to be deleted and words that will remain in a compressed sentence. We also introduce a graph neural network (GNN) in order to employ dependency tree structures when compressing a sentence. A dependency tree is encoded by a graph convolutional network (GCN), one of the most common GNNs, and every node in the encoded tree is input into the sentence compression module. As a conventional GCN deals with only undirected graphs, we propose a directed graph convolutional network (D-GCN) to differentiate between parent and child nodes of a dependency tree in sentence compression. Experimental results show that the baseline model is improved in terms of the sentence compression accuracy when employing a GNN. Regarding the performance comparison of graph networks, a D-GCN achieves higher F1 scores than a GCN when applied to sentence compression. Through experiments, it is confirmed that better performance can be achieved for sentence compression when the dependency syntax tree structure is explicitly reflected.
Building a Korean Sentence-Compression Corpus by Analyzing Sentences and Deleting Words
GyoungHo Lee, Yo-Han Park, Kong Joo Lee
http://doi.org/10.5626/JOK.2021.48.2.183
Developing a sentence-compression system based on deep learning models requires a parallel corpus consisting of both original sentences and compressed sentences. In this paper, we propose a sentence-compression algorithm that can compress an original sentence into a short sentence. Our basic approach is to delete nodes from a syntactic-dependency tree of the original sentence while maintaining the grammaticality of the compressed sentence. The algorithm chooses nodes to be deleted using the structural constraints and semantically obligatory information of the sentence. By applying the algorithm to the first sentences and headlines of news articles, we built a Korean sentence-compression corpus consisting of approximately 140,000 pairs. We manually assessed the quality of the compression in terms of readability and informativeness, which yielded results of 4.75 and 4.53 out of 5, respectively.
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