Topic-Focused Summarization of Chat Conversations
In this paper, we propose a novel approach to address the problem of chat summarization. We summarize real-time chat conversations which contain multiple users with frequent shifts in topic. Our approach consists of two phases. In the first phase, we leverage topic modeling using web documents to find the primary topic of discussion in the chat. Then, in the summary generation phase, we build a semantic word space to score sentences based on their association with the primary topic. Experimental results show that our method significantly outperforms the baseline systems on ROUGE F-scores.
KeywordsTopic Modeling Latent Dirichlet Allocation Baseline System Primary Topic Topic Distribution
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- 2.Gillick, D., Riedhammer, K., Favre, B., Hakkani-Tur, D.Z.: A global optimization framework for meeting summarization. In: International Conference on Acoustics, Speech and Signal Processing, pp. 4769–4772 (2009)Google Scholar
- 3.Jagadeesh, J., Pingali, P., Varma, V.: A relevance-based language modeling approach to duc 2005. In: Document Understanding Conference (DUC 2005) (2005)Google Scholar
- 4.Lowe, W., McDonald, S.: The direct route: Mediated priming in semantic space. In: Annual Conference of the Cognitive Science Society, pp. 806–811 (2002)Google Scholar
- 5.Zhou, L., Hovy, E.H.: Digesting virtual geek culture: The summarization of technical internet relay chats. In: Meeting of the Association for Computational Linguistics, pp. 298–305 (2005)Google Scholar