Sentence Ordering

Ordering information is a difficult but important task for applications generat- ing natural-language text. We present a bottom-up approach to arranging sen- tences extracted for multi-document sum- marization. To capture …...

We extend the original entity-based coher- ence model (Barzilay and Lapata, 2008) by learning from more fine-grained coher- ence preferences in training data. We asso- ciate multiple ranks with the …...

We propose a computationally efficient graph-based approach for local coherence modeling. We evaluate our system on three tasks: sentence ordering, summary coherence rating and readability assess- ment. The performance is …...

This paper presents G-FLOW, a novel system for coherent extractive multi-document sum- marization (MDS).1 Where previous work on MDS considered sentence selection and or- dering separately, G-FLOW introduces a joint …...

AbstractIn this paper, to evaluate text coherence, we propose the paragraph ordering task as well as conducting sentence ordering. We collected four distinct corpora from different domains on which we …...

AbstractSentence ordering is the task of arranging the sentences of a given text in the correct order. Recent work using deep neural networks for this task has framed it as …...

AbstractDominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models, which inituitively possess complementary …...

AbstractThe task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine’s understanding of causal and temporal relations. We formulate the sentence …...

AbstractUnderstanding narrative flow and text coherence in long-form documents (novels) remains an open problem in NLP.To gain insight, we explore the task of chapter ordering, reconstructing the original order of …...