For the last decade, text mining techniques, which discover knowledgefrom large amounts of text data, have been making a significant
progress. While text mining techniques are useful for independenttexts, they don't work well for email and BBS (bulletin board systems)
discussions because of the incompleteness of each individual message.Therefore, we need a new method to extract useful information from
these kinds of text fragments. This paper describes methods ofstructuring discussion records or logs, such as email and BBS
messages, allowing knowledge discovery using various retrieval and visualization techniques. Each message mainly consists of quotations
and comments, but based on the quote-comment relationships, our proposed system generates a Thread Summary which is an abstraction of
the ongoing discussion on a particular topic. We consider the Thread Summary to be a coherent document and produce customized summaries to
allow users to find their topics of interest more easily. Also, our system can recognize an authority, a person who has a good knowledge
in a particular field based on patterns of message exchange.
By: Akiko Murakami, Katashi Nagao, Koichi Takeda
Published in: RT0422 in 2002
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