期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2010
卷号：2010
出版社：ACL Anthology
摘要：This paper presents a framework for automatically
processing information coming
from community Question Answering
(cQA) portals with the purpose of generating
a trustful, complete, relevant and
succinct summary in response to a question.
We exploit the metadata intrinsically
present in User Generated Content (UGC)
to bias automatic multi-document summarization
techniques toward high quality information.
We adopt a representation of
concepts alternative to n-grams and propose
two concept-scoring functions based
on semantic overlap. Experimental results
on data drawn from Yahoo! Answers
demonstrate the effectiveness of our
method in terms of ROUGE scores. We
show that the information contained in the
best answers voted by users of cQA portals
can be successfully complemented by
our method.
