Abstract
In this paper, we report the first work ever of detecting the summarizable chat conversation in order to improve the quality of summarization and system performance, especially in real time server-based system like online instant messaging. Summarizable chat conversation means that the document assessed could produce a meaningful summary for human. Our study intends to answer the question: what are the characteristics of a summarizable chat and how to distinguish it with non-summarizable chat conversation. To conduct the experiment, corpora of 536 chat conversations was constructed manually. Technically, we used 19 attributes and grouped them by feature sets of (1) chat attribute, (2) lexical, and (3) Rapid Automatic Keyword Extraction (RAKE). As result, our work reveals that the features can classify summarizable chat by 78.36 % as our highest accuracy, performed by feature selection with SVM.
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Koto, F., Abdillah, O. (2016). Automatic Advisor for Detecting Summarizable Chat Conversations in Online Instant Messages. In: Meesad, P., Boonkrong, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2016. Advances in Intelligent Systems and Computing, vol 463. Springer, Cham. https://doi.org/10.1007/978-3-319-40415-8_5
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DOI: https://doi.org/10.1007/978-3-319-40415-8_5
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