Paper: Unsupervised Learning Summarization Templates from Concise Summaries

ACL ID N13-1027
Title Unsupervised Learning Summarization Templates from Concise Summaries
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

We here present and compare two unsuper- vised approaches for inducing the main con- ceptual information in rather stereotypical summaries in two different languages. We evaluate the two approaches in two differ- ent information extraction settings: mono- lingual and cross-lingual information extrac- tion. The extraction systems are trained on auto-annotated summaries (containing the in- duced concepts) and evaluated on human- annotated documents. Extraction results are promising, being close in performance to those achieved when the system is trained on human-annotated summaries.