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 describe a model that learns constraints on sentence order from a corpus of domainspecific texts and an algorithm that yields the most likely order among several alternatives. We evaluate the automatically generated orderings against authored texts from our corpus and against human subjects that are asked to mimic the model's task. We also assess the appropriateness of such a model for multidocument summarization.
For works taking no use of source document, REF proposed a probabilistic model which learns constraints on sentence ordering from a corpus of texts.
Probabilistic Text Structuring: Experiments With Sentence Ordering
Annual Meeting Of The Association For Computational Linguistics
Computer Science