Paper: Metadata-Aware Measures for Answer Summarization in Community Question Answering

ACL ID P10-1078
Title Metadata-Aware Measures for Answer Summarization in Community Question Answering
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

This paper presents a framework for au- tomatically processing information com- ing from community Question Answering (cQA) portals with the purpose of gen- erating a trustful, complete, relevant and succinct summary in response to a ques- tion. We exploit the metadata intrinsically present in User Generated Content (UGC) to bias automatic multi-document summa- rization techniques toward high quality in- formation. We adopt a representation of concepts alternative to n-grams and pro- pose two concept-scoring functions based on semantic overlap. Experimental re- sults on data drawn from Yahoo! An- swers 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 por- tals can be successfully complemen...