End-to-End Neural Sentence Ordering Using Pointer Network

Abstract

Sentence ordering is one of important tasks in NLP. Previous works mainly focused on improving its performance by using pair-wise strategy. However, it is nontrivial for pair-wise models to incorporate the contextual sentence information. In addition, error prorogation could be introduced by using the pipeline strategy in pair-wise models. In this paper, we propose an end-to-end neural approach to address the sentence ordering problem, which uses the pointer network (Ptr-Net) to alleviate the error propagation problem and utilize the whole contextual information. Experimental results show the effectiveness of the proposed model. Source codes and dataset of this paper are available.

References in corpus (4)

• Inferring Strategies for Sentence Ordering in Multidocument News Summarization
• Visualizing and Understanding Neural Models in NLP
• Neural Sentence Ordering
• Neural Net Models for Open-Domain Discourse Coherence

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