Paper: Fully Abstractive Approach to Guided Summarization

ACL ID P12-2069
Title Fully Abstractive Approach to Guided Summarization
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

Fully Abstractive Approach to Guided Summarization Pierre-Etienne Genest, Guy Lapalme RALI-DIRO Universite? de Montre?al P.O. Box 6128, Succ. Centre-Ville Montre?al, Que?bec Canada, H3C 3J7 {genestpe,lapalme}@iro.umontreal.ca Abstract This paper shows that full abstraction can be accomplished in the context of guided sum- marization. We describe a work in progress that relies on Information Extraction, statis- tical content selection and Natural Language Generation. Early results already demonstrate the effectiveness of the approach.