Paper: Towards Robust Abstractive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain

ACL ID P13-1121
Title Towards Robust Abstractive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Towards Robust Abstractive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain Jackie Chi Kit Cheung University of Toronto 10 King?s College Rd., Room 3302 Toronto, ON, Canada M5S 3G4 jcheung@cs.toronto.edu Gerald Penn University of Toronto 10 King?s College Rd., Room 3302 Toronto, ON, Canada M5S 3G4 gpenn@cs.toronto.edu Abstract In automatic summarization, centrality is the notion that a summary should contain the core parts of the source text. Cur- rent systems use centrality, along with re- dundancy avoidance and some sentence compression, to produce mostly extrac- tive summaries. In this paper, we investi- gate how summarization can advance past this paradigm towards robust abstraction by making greater use of the domain of the source text. We conduct a series o...