Abstract
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.
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Sood, A., Mohamed, T.P., Varma, V. (2013). Topic-Focused Summarization of Chat Conversations. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_88
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DOI: https://doi.org/10.1007/978-3-642-36973-5_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
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