A bottom-up approach to sentence ordering for multi-document summarization

Danushka Bollegala, Naoaki Okazaki, Mitsuru Ishizuka
2006 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06  
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
more » ... 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.
doi:10.3115/1220175.1220224 dblp:conf/acl/BollegalaOI06 fatcat:7piixnfj7bd6jd26zl7mvy3qtq