title: Sentence Ordering using Recurrent Neural Networks
abstract: Modeling the structure of coherent texts is a task of great importance in NLP. The task of organizing a given set of sentences into a coherent order has been
  commonly used to build and evaluate models that understand such structure. In this work we propose an end-to-end neural approach based on the recently proposed
  set to sequence mapping framework to address the sentence ordering problem. Our model achieves state-of-the-art performance in the order discrimination task
  on two datasets widely used in the literature. We also consider a new interesting task of ordering abstracts from conference papers and research proposals and
  demonstrate strong performance against recent methods. Visualizing the sentence representations learned by the model shows that the model has captured high
  level logical structure in these paragraphs. The model also learns rich semantic sentence representations by learning to order texts, performing comparably to
  recent unsupervised representation learning methods in the sentence similarity and paraphrase detection tasks.
authors: Lajanugen Logeswaran Honglak Lee Dragomir Radev
hIndex: Honglak Lee 79