Sentence Ordering and Coherence Modeling using Recurrent Neural Networks

Understand

Modeling the structure of coherent texts is a key NLP problem.

• The task of coherently organizing a given set of sentences has been commonly used to build and evaluate models that understand such structure.
• We propose an end-to-end unsupervised deep learning approach based on the set-to-sequence framework to address this problem.
• Our model strongly outperforms prior methods in the order discrimination task and a novel task of ordering abstracts from scientific articles.