There is a topic mentioned in the book on Survey of Text Mining as HotMiner by Malu Castellanos. Here companies would like to find topics that are of interest to their customers i.e. hot problems and making them available on their website  along with links to corresponding solution documents. The customer support centers maintain logs of their customer interactions which becomes the source for discovering hot topics. The author uses these case logs to extract relevant sentences from cases to conform case excerpts. In addition, this approach deals with dirty text containing typos, adhoc abbreviations, incorrect grammar, cryptic tables and ambiguous and missing punctuations. They normalize the terminology with a thesaurus assistant and a sentence identifier.
The suggestion here is that the logs rather than the documents provide information on the topics that are of most interest to the customers. These are called hot topics. This works well given that the document categorization and classification - be it manual or automatic, is not sufficient to detect the topics of interest to the customers. Instead by providing the self-help solution documents by identifying the topics of these hot problems, organizations can better organize their site, reduce customer support costs and improve their customer satisfaction.
The author's approach to mine hot topics from logs of customer support centers, involves the use of two kinds of logs  - a search log and a case log. The search log keeps track of the search strings that customers formulate and the case logs keep track of the cases opened by the customers along with the history of action, events and dialogues, followed while a case is open.
These two logs are complimentary to obtain information on all problems encountered by the customer.