Paper: Learning to Order Natural Language Texts

ACL ID P13-2016
Title Learning to Order Natural Language Texts
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013

Ordering texts is an important task for many NLP applications. Most previous works on summary sentence ordering rely on the contex- tual information (e.g. adjacent sentences) of each sentence in the source document. In this paper, we investigate a more challenging task of ordering a set of unordered sentences with- out any contextual information. We introduce a set of features to characterize the order and coherence of natural language texts, and use the learning to rank technique to determine the order of any two sentences. We also propose to use the genetic algorithm to determine the total order of all sentences. Evaluation results on a news corpus show the effectiveness of our proposed method.