Paper: Domain-Independent Abstract Generation for Focused Meeting Summarization

ACL ID P13-1137
Title Domain-Independent Abstract Generation for Focused Meeting Summarization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Domain-Independent Abstract Generation for Focused Meeting Summarization Lu Wang Department of Computer Science Cornell University Ithaca, NY 14853 luwang@cs.cornell.edu Claire Cardie Department of Computer Science Cornell University Ithaca, NY 14853 cardie@cs.cornell.edu Abstract We address the challenge of generating natu- ral language abstractive summaries for spoken meetings in a domain-independent fashion. We apply Multiple-Sequence Alignment to in- duce abstract generation templates that can be used for different domains. An Overgenerate- and-Rank strategy is utilized to produce and rank candidate abstracts. Experiments us- ing in-domain and out-of-domain training on disparate corpora show that our system uni- formly outperforms state-of-the-art supervised extract-based approaches. I...