Abstract
Ordering information is a difficult but important task for applications generating natural-language text. We present a bottom-up approach to arranging sentences extracted for multi-document summarization. To capture the association and order of two textual segments (eg, sentences), we define four criteria, chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion until we obtain the overall segment with all sentences arranged. Our experimental results show a significant improvement over existing sentence ordering strategies.
| Original language | English |
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| Title of host publication | COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference |
| Pages | 385-392 |
| Number of pages | 8 |
| Volume | 1 |
| Publication status | Published - 2006 |
| Externally published | Yes |
| Event | 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, NSW Duration: 2006 Jul 17 → 2006 Jul 21 |
Other
| Other | 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 |
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| City | Sydney, NSW |
| Period | 06/7/17 → 06/7/21 |
ASJC Scopus subject areas
- Language and Linguistics
- Linguistics and Language