Paper: Community Answer Summarization for Multi-Sentence Question with Group L1 Regularization

ACL ID P12-1061
Title Community Answer Summarization for Multi-Sentence Question with Group L1 Regularization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

We present a novel answer summarization method for community Question Answering services (cQAs) to address the problem of ?in- complete answer?, i.e., the ?best answer? of a complex multi-sentence question misses valu- able information that is contained in other an- swers. In order to automatically generate a novel and non-redundant community answer summary, we segment the complex original multi-sentence question into several sub ques- tions and then propose a general Conditional Random Field (CRF) based answer summary method with group L1 regularization. Vari- ous textual and non-textual QA features are explored. Specifically, we explore four differ- ent types of contextual factors, namely, the in- formation novelty and non-redundancy mod- eling for local and non-local sentence inter- act...