Paper: Automatic Generation of Story Highlights

ACL ID P10-1058
Title Automatic Generation of Story Highlights
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

In this paper we present a joint con- tent selection and compression model for single-document summarization. The model operates over a phrase-based rep- resentation of the source document which we obtain by merging information from PCFG parse trees and dependency graphs. Using an integer linear programming for- mulation, the model learns to select and combine phrases subject to length, cover- age and grammar constraints. We evalu- ate the approach on the task of generat- ing “story highlights”—a small number of brief, self-contained sentences that allow readers to quickly gather information on news stories. Experimental results show that the model’s output is comparable to human-written highlights in terms of both grammaticality and content.