期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2012
卷号：2012
出版社：ACL Anthology
摘要：We present a novel answer summarization
method for community Question Answering
services (cQAs) to address the problem of ¡°incomplete
answer¡±, i.e., the ¡°best answer¡± of a
complex multi-sentence question misses valuable
information that is contained in other answers.
In order to automatically generate a
novel and non-redundant community answer
summary, we segment the complex original
multi-sentence question into several sub questions
and then propose a general Conditional
Random Field (CRF) based answer summary
method with group L1 regularization. Various
textual and non-textual QA features are
explored. Specifically, we explore four different
types of contextual factors, namely, the information
novelty and non-redundancy modeling
for local and non-local sentence interactions
under question segmentation. To further
unleash the potential of the abundant cQA
features, we introduce the group L1 regularization
for feature learning. Experimental
results on a Yahoo! Answers dataset show
that our proposed method significantly outperforms
state-of-the-art methods on cQA summarization
task.
