TY - GEN
T1 - Using concept-level random walk model and global inference algorithm for answer summarization
AU - Liu, Xiaoying
AU - Li, Zhoujun
AU - Zhao, Xiaojian
AU - Zhou, Zhenggan
PY - 2011
Y1 - 2011
N2 - Community Question Answer (cQA) archives contain rich sources of knowledge on extensive topics, in which the quality of the submitted answer is uneven, ranging from excellent detailed answers to completely unrelated content. We propose a framework to generate complete, relevant, and trustful answer summaries. The framework discusses answer summarization in terms of maximum coverage problem with knapsack constraint on conceptual level. Global inference algorithm is employed to extract sentences according to the saliency scores of concepts. The saliency score of each concept is assigned through a two-layer graph-based random walk model incorporating the user social features and text content from answers. The experiments are implemented on a data set from Yahoo! Answer. The results show that our method generates satisfying summaries and is superior to the state-of-the-art approaches in performance.
AB - Community Question Answer (cQA) archives contain rich sources of knowledge on extensive topics, in which the quality of the submitted answer is uneven, ranging from excellent detailed answers to completely unrelated content. We propose a framework to generate complete, relevant, and trustful answer summaries. The framework discusses answer summarization in terms of maximum coverage problem with knapsack constraint on conceptual level. Global inference algorithm is employed to extract sentences according to the saliency scores of concepts. The saliency score of each concept is assigned through a two-layer graph-based random walk model incorporating the user social features and text content from answers. The experiments are implemented on a data set from Yahoo! Answer. The results show that our method generates satisfying summaries and is superior to the state-of-the-art approaches in performance.
UR - https://www.scopus.com/pages/publications/84255175461
U2 - 10.1007/978-3-642-25631-8_39
DO - 10.1007/978-3-642-25631-8_39
M3 - 会议稿件
AN - SCOPUS:84255175461
SN - 9783642256301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 434
EP - 445
BT - Information Retrieval Technology - 7th Asia Information Retrieval Societies Conference, AIRS 2011, Proceedings
T2 - 7th Asia Information Retrieval Societies Conference, AIRS 2011
Y2 - 18 December 2011 through 20 December 2011
ER -