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.