Using Concept-Level Random Walk Model and Global Inference Algorithm for Answer Summarization

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.