Summarizing answers in non-factoid community question-answering

We aim at summarizing answers in community question-answering (CQA). While most previous work focuses on factoid questionanswering, we focus on the non-factoid question-answering. Unlike factoid CQA, non-factoid question-answering usually requires passages as answers. The shortness, sparsity and diversity of answers form interesting challenges for summarization. To tackle these challenges, we propose a sparse coding-based summarization strategy that includes three core ingredients: short document expansion, sentence vectorization, and a sparse-coding optimization framework. Specifically, we extend each answer in a questionanswering thread to a more comprehensive representation via entity linking and sentence ranking strategies. From answers extended in this manner, each sentence is represented as a feature vector trained from a short text convolutional neural network model. We then use these sentence representations to estimate the saliency of candidate sentences via a sparse-coding framework that jointly considers candidate sentences and Wikipedia sentences as reconstruction items. Given the saliency vectors for all candidate sentences, we extract sentences to generate an answer summary based on a maximal marginal relevance algorithm. Experimental results on a benchmark data collection confirm the effectiveness of our proposed method in answer summarization of non-factoid CQA, and moreover, its significant improvement compared to state-of-the-art baselines in terms of ROUGE metrics.