Improving a Syntactic Graph Convolution Network for Sentence Compression

Wang, Yifan; Chen, Guang

doi:10.1007/978-3-030-32381-3_11

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2021

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“…Sentence compression is currently dominated by supervised methods (Malireddy et al, 2020;Nguyen et al, 2020;Nóbrega et al, 2020) and highly relies on syntactic dependency trees (Le et al, 2019;Xu and Durrett, 2019b;Wang and Chen, 2019;Kamigaito and Okumura, 2020). Unsupervised methods have been explored to extract sentences from documents to represent the key points (Jang and Kang, 2021).…”

Section: Related Workmentioning

confidence: 99%

A Novel Metric for Evaluating Semantics Preservation

Peng¹,

Li²,

Zhao³

2021

Preprint

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In this paper, we leverage pre-trained language models (PLMs) to precisely evaluate the semantics preservation of edition process on sentences. Our metric, Neighbor Distribution Divergence (NDD), evaluates the disturbance on predicted distribution of neighboring words from mask language model (MLM). NDD is capable of detecting precise changes in semantics which are easily ignored by text similarity. By exploiting the property of NDD, we implement a unsupervised and even trainingfree algorithm for extractive sentence compression. We show that our NDD-based algorithm outperforms previous perplexity-based unsupervised algorithm by a large margin. For further exploration on interpretability, we evaluate NDD by pruning on syntactic dependency treebanks and apply NDD for predicate detection as well.

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