With increasing adoption of Internet and various social media technologies by companies, the web has become a rich source of information about many aspects of organizational activities. Hence one input towards gathering competitive intelligence is to mine the text sources available on the web for any particular company and use the summarized version of that information for strategic decision making. This paper discusses a methodological approach and an architecture for such summarization system which can be used for gathering competitor intelligence by the companies.

Shubhamoy Dey

Information Systems

Chakraborti S

<p>IIM Indore seeks to be a contextually-relevant business school with world-class academic standards that develops socially-conscious managers, leaders and entrepreneurs. IIM Indore is committed to: Excellence in management education, research, and training, Use of contemporary participant-centric pedagogies and teaching methods, A presence in emerging segments of management education.</p>

IIM Indore

Multi-document Text Summarization for Competitor Intelligence: A Methodology

Proceedings  2014 2nd International Symposium on Computational and Business Intelligence, ISCBI 2014

Automatic text summarization can be applied to extract summaries from competitor intelligence (CI) corpora that organizations create by gathering textual data from the Internet. Such a representation of CI text is easier for managers to interpret and use for making decisions. This research investigates design of an integrated system for CI analysis which comprises clustering and automatic text summarization and evaluates quality of extractive summaries generated automatically by various text-summarization techniques based on global optimization. This research is conducted using experimentation and empirical analysis of results. A survey of practicing managers is also carried out to understand the effectiveness of automatically generated summaries from CI perspective. Firstly, it shows that global optimization-based techniques generate good quality extractive summaries for CI analysis from topical clusters created by the clustering step of the integrated system. Secondly, it shows the usefulness of the generated summaries by having them evaluated by practicing managers from CI perspective. Finally, the implication of this research from the point of view of theory and practice is discussed.

Fulltext

Business and Information Systems Engineering

Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

<p>Proliferation of web as an easily accessible information resource has led many corporations to gather competitor intelligence from the internet. While collection of such information is easy from internet, the collation and structuring of them for perusal of business decision makers, is a real trouble. Text clustering based topic identification techniques are expected to be very useful for such application. Using appropriate clustering technologies, the competitor intelligence corpus, gathered from the web, can be divided into topical groups and henceforth the analysis of this information becomes comparatively easier for the managers. This paper presents a study on the effectiveness of standard K-means text clustering algorithm applied at multiple levels, in a top-down, divide-and-conquer fashion, on competitor intelligence corpus, created from publicly available sources on the web, such as news, blogs, research papers etc. The paper also demonstrates the capability of Multi-level K-means (ML-KM) clustering technique to determine the optimal number of clusters as part of clustering process. The cluster validity metric used to determine cluster quality has also been explained along with other user-controlled configuration parameters. It is empirically found that ML-KM technique also addresses one problem of stand-alone standard K-means (S-KM), which is its bias towards convex, spherical clusters, resulting in bigger clusters subsuming smaller ones. This specific advantage of ML-KM over stand-alone S-KM to detect smaller clusters, makes it more suitable for clustering competitor intelligence related text corpus where niche, smaller clusters can actually lead to important findings. The experimental results are presented for both ML-KM and stand-alone S-KM clustering techniques based on competitor intelligence corpus as well as the standard Reuters corpus.</p>

Proceedings  International Conference on Research Challenges in Information Science

Multi-level K-means text clustering technique for topic identification for competitor intelligence

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.

Journal	Data powered by TypesetProceedings 2014 2nd International Symposium on Computational and Business Intelligence, ISCBI 2014
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No