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Online discussion forums are prevalent and easily accessible, thus allowingpeople to share ideas and opinions by posting messages in the discussionthreads. Forum threads that significantly grow in length can become difficultfor participants, both newcomers and existing, to grasp main ideas. To mitigatethis problem, this study aims to create an automatic text summarizer for onlineforums. We present Hierarchical Unified Deep Neural Network to build sentenceand thread representations for the forum summarization. In this scheme, Bi-LSTMderives a representation that comprises information of the whole sentence andwhole thread; whereas, CNN captures most informative features with respect tocontext from sentence and thread. Attention mechanism is applied on top of CNNto further highlight high-level representations that carry importantinformation contributing to a desirable summary. Extensive performanceevaluation has been conducted on three datasets, two of which are real-lifeonline forums and one is news dataset. The results reveal that the proposedmodel outperforms several competitive baselines.

Improving Online Forums Summarization via Hierarchical Unified Deep Neural Network