Neural Sentence Ordering

Abstract

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.

References in corpus (3)

• ADADELTA: An Adaptive Learning Rate Method
• Inferring Strategies for Sentence Ordering in Multidocument News Summarization
• Visualizing and Understanding Neural Models in NLP

Cited by in corpus (14)

• StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
• End-to-End Neural Sentence Ordering Using Pointer Network
• Discriminative Sentence Modeling for Story Ending Prediction
• Neural Sentence Ordering Based on Constraint Graphs
• Local and Global Context-Based Pairwise Models for Sentence Ordering
• Evaluating Text Coherence at Sentence and Paragraph Levels
• Topological Sort for Sentence Ordering
• Context is Key: New Approaches to Neural Coherence Modeling
• InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?
• Learning Sentence Embeddings for Coherence Modelling and Beyond
• BERT4SO: Neural Sentence Ordering by Fine-tuning BERT
• Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations
• A New Sentence Ordering Method Using BERT Pretrained Model
• Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings