Paper: Probabilistic Text Structuring: Experiments With Sentence Ordering

ACL ID P03-1069
Title Probabilistic Text Structuring: Experiments With Sentence Ordering
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

Ordering information is a critical task for natural language generation applications. In this paper we propose an approach to information ordering that is particularly suited for text-to-text generation. We de- scribe a model that learns constraints on sentence order from a corpus of domain- specific texts and an algorithm that yields the most likely order among several al- ternatives. We evaluate the automatically generated orderings against authored texts from our corpus and against human sub- jects that are asked to mimic the model’s task. We also assess the appropriateness of such a model for multidocument summa- rization.