Neural Sentence Ordering

By Xinchi Chen, Xipeng Qiu, Xuanjing Huang. 2016

70 citations recorded by the source corpus.

Sentence ordering is a general and critical task for natural language generation applications. Previous works have focused on improving its performance in an external, downstream task, such as multi-document summarization. Given its importance, we propose to study it as an isolated task. We collect a large corpus of academic texts, and derive a data driven approach to learn pairwise ordering of sentences, and validate the efficacy with extensive experiments. Source codes and dataset of this paper will be made publicly available.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019, background): “The goal of sentence ordering is to arrange a set of sentences into a coherent text in a clear and consistent manner which can be viewed as a ranking problem [17].”
Sentence Ordering using Recurrent Neural Networks (2016, unknown): “A similar observation was made by Chen et al. (2016) in their experiments.”
SLM: Learning a Discourse Language Representation with Sentence Unshuffling (2020, methodology): “Inspired by prior works about sentence-based representations for recurrent networks (Kiros et al., 2015; Hill et al., 2016; Gan et al., 2017; Jernite et al., 2017; Logeswaran et al., 2018; Gong et al., 2016; Chen et al., 2016), we seek to incorporate a more explicit hierarchy into the transformer by”
NAREOR: The Narrative Reordering Problem (2021, background): “For example, Chen, Qiu, and Huang (2016) learn pairwise orderings of sentences using a ranking model.”