Our main contributions are that we proposed a systematic way for modeling semantic contextual interactions between the answer sentences based on question segmentation and we explored both the textual and nontextual answer features learned via a group L 1 regularization.
there is no previous work that explores the complex multi-sentence question segmentation and its contextual modeling for community answer summarization.