Abstract
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 domain-specific 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.
| Original language | English |
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| Title of host publication | Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics |
| Publisher | Association for Computational Linguistics |
| Pages | 545-552 |
| Number of pages | 8 |
| Publication status | Published - 2003 |
| Event | 41st Annual Meeting of the Association for Computational Linguistics - Sapporo Convention Centre, Sapporo, Japan Duration: 7 Jul 2003 → 12 Jul 2003 |
Conference
| Conference | 41st Annual Meeting of the Association for Computational Linguistics |
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| Country/Territory | Japan |
| City | Sapporo |
| Period | 7/07/03 → 12/07/03 |