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Product news summarization for competitor intelligence using topic identification and artificial bee colony optimization
Chakraborti S,
Published in Association for Computing Machinery
Pages: 1 - 6
With proliferation of web content, nowadays, various information about companies have become publicly available online. These information are mostly text documents such as news, reports, which can provide useful insight into various aspects about corporations. In order to extract useful information from this huge and diverse collection of texts, appropriate state-of-the-art text mining techniques are necessary. In this paper, a novel multi-document extractive text summarization technique, based on topic identification and artificial bee colony optimization, is described which can be used by companies for extracting important facts from the product-specific news items of their competitors and subsequently use them as one of the inputs for strategic business decision making. The results presented in this paper are based on the corpus created by collecting news items for a specific consumer electronics company from authentic news sites available on the internet. The quality of summary generated using this approach is found to be better on many aspects as compared to summaries generated by a well-known benchmark summarizer called MEAD.
About the journal
JournalData powered by TypesetProceeding of the 2015 Research in Adaptive and Convergent Systems, RACS 2015
PublisherData powered by TypesetAssociation for Computing Machinery
Open AccessNo