Using Sparse Coding for Answer Summarization in Non Factoid Community Question Answering
We focus on the task of summarizing answers in community ques tion answering (CQA) While most previous work on answer sum marization focuses on factoid question answering, we focus on non factoid question answering. In contrast to factoid CQA with a short and accurate answer, non factoid question answering usually re quires passages as answers. The diversity, shortness and sparse ness of answers form interesting challenges for summarization. To tackle these challenges, we propose a sparse coding based summa rization strategy, in which we can effectively capture the saliency of diverse, short and sparse units. Specifically, after transferring all candidate answer sentences into vectors, we present a coordinate descent learning method to optimize a loss function to reconstruct the input vectors as a linear combination of basis vectors. Experi mental results on a benchmark data collection confirm the effective ness of our proposed method in non factoid CQA summarization. Our method is shown to significantly outperform the state of the art in terms of ROUGE metrics.