Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Business & Information Systems Engineering
Published in: Business & Information Systems Engineering 3/2019

16-10-2018 | Research Paper

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

Authors: Swapnajit Chakraborti, Shubhamoy Dey

Published in: Business & Information Systems Engineering | Issue 3/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

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.
previous article Prognostic Model Development with Missing Labels
next article Conceptual Framework for SDSS Development with an Application in the Retail Industry

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Show more products
Literature
Ackermann M, Soares C, Guidemann B (2006) Practical data mining: applications, experiences and challenges. In: SAS & PKDD workshop, Berlin
Alguliev RM, Aliguliyev RM, Mehdiyev CA (2011a) Sentence selection for generic document summarization using an adaptive differential evolution algorithm. Swarm Evol Comput 1(4):213–222CrossRef
Alguliev RM, Aliguliyev RM, Hajirahimova MS, Mehdiyev CA (2011b) MCMR: maximum coverage and minimum redundant text summarization model. Expert Syst Appl 38(12):14514–14522CrossRef
Alguliev RM, Aliguliyev RM, Isazade NR (2012) DESAMC + DocSum: differential evolution with self-adaptive mutation and crossover parameters for multi-document summarization. Knowl Based Syst 36:21–38CrossRef
Alguliev RM, Aliguliyev RM, Isazade NR (2013) CDDS: constraint-driven document summarization models. Expert Syst Appl 40(2):458–465CrossRef
Alguliev RM, Aliguliyev RM, Isazade NR (2014) Multiple documents summarization based on evolutionary optimization algorithm. Expert Syst Appl 40(5):1675–1689CrossRef
Allan J, Carbonell J, Doddington G, Yamron J, Yang Y (2000) Topic detection and tracking pilot study final report. DARPA, Arlington
Arnott D, Pervan G (2008) Eight key issues for the decision support systems discipline. Decis Support Syst 44(3):657–672CrossRef
Baralis E, Cagliero L, Jabeen S, Fiori A, Shah S (2013) Multi-document summarization based on Yago ontology. Expert Syst Appl 40(17):6976–6984CrossRef
Barzilay R, Elhadad M (1997) Using lexical chains for text summarization. In: Proceedings ISTS, pp 10–17
Bellegarda J, Butzberger JW, Chow Y, Coccaro NB, Naik D (1996) A novel word clustering algorithm based on latent semantic analysis. In: ICASSP, vol 1. IEEE, pp 172–175
Bitzer P, Söllner M, Leimeister JM (2015) Design principles for high-performance blended learning services delivery. Bus Inf Syst Eng 58(2):135–149CrossRef
Bland JM, Altman DG (1995) Multiple significance tests: the Bonferroni method. BMJ 310:170CrossRef
Blei DM (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022
Browne GJ, Cheung C, Heinzl A, Riedl R (2017) Human information behavior. Bus Inf Syst Eng 59(1):1–2CrossRef
Carbonell J, Goldstein J (1998) The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of SIGIR, pp 335–336
Carullo MB, Binaghi E, Gallo I (2009) An online document clustering technique for short web contents. Pattern Recognit Lett 30(10):870–876CrossRef
Chakraborti S (2015) Multi-document text summarization for competitor intelligence: a methodology based on topic identification and artificial bee colony optimization. SAC, ACM Digital Library, Salamanca, pp 1110–1111
Chakraborti S, Dey S (2014) Multi-document text summarization for competitor intelligence: a methodology. In: ISCBI-2014. IEEE Computer Society, New Delhi, pp 97–100
Chakraborti S, Dey S (2015) Product news summarization for competitor intelligence using topic identification and artificial bee colony optimization. ACM RACS, ACM Digital Library, Prague, pp 1–6
Chakraborti S, Dey S (2016) Multi-level k-means text clustering technique for topic identification for competitor intelligence. In: Proceedings of RCIS. IEEE, Grenoble, pp 1–10
Codina-Filbà J, Bouayad-Agha N, Burga A, Casamayor G, Wanner L (2017) Using genre-specific features for patent summaries. Inf Process Manag 53(1):151–174CrossRef
Cohen W, Levinthal D (1990) Absorptive capacity: a new perspective on learning and innovation. Adm Sci Q 35(1):128–152CrossRef
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Method Appl Mech Eng 186(2–4):311–338CrossRef
Deerwester S (1990) Indexing by latent semantic analysis. JOASIS 41(6):391–407CrossRef
DeJong GF (1978) Fast skimming of news stories: the FRUMP system. PhD thesis, Yale University
Dumais S, Chen H (2000) Hierarchical classification of web content. In: SIGIR Conference on research and development in information retrieval. ACM, pp 256–263
Erkan G, Radev DR (2004) LexRank: graph-based lexical centrality as salience in text summarization. J Artif Intell Res 22:457–479CrossRef
Fischer C, Winter R, Wortmann F (2010) Design theory. Bus Inf Syst Eng 2(6):387–390CrossRef
Flath C, Nicolay D, Conte T, Dinther C, Filipova-Neumann L (2012) Cluster analysis of smart metering data. Bus Inf Syst Eng 4(1):31–39CrossRef
Gilad B (2015) Companies collect competitive intelligence but don’t use it. Harv Bus Rev
Gilad B, Fuld L (2016) Only half of companies actually use the competitive intelligence they collect. Harv Bus Rev
Groom JR, David FR (2001) Competitive intelligence activity among small firms. SAM Adv Manag J 66(1):12–20
Halko J, Martinsson P, Tropp J (2010) Finding structure with randomness: probabilistic algorithm for constructing approximate matrix decomposition. SIAM Rev 53(2):217–288CrossRef
Heinrich P, Schwabe G (2017) Facilitating informed decision-making in financial service encounters. Bus Inf Syst Eng 60(4):317–329CrossRef
Hevner AR (2007) A three cycle view of design science research. Scand J Inf Syst 19(2):87–92
Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105CrossRef
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Hornik K, Kober M, Feinerer I, Buchta C (2012) Spherical k-means clustering. J Stat Softw 50(10):1–22CrossRef
Hu Y, Chen Y, Chou H (2017) Opinion mining from online hotel reviews – a text summarization approach. Inf Process Manag 53(2):436–449CrossRef
Jain AM, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRef
Jishma Mohan M, Sunitha C, Ganesh A, Jaya A (2016) A study on ontology based abstractive summarization. Proc Comput Sci 87:32–37CrossRef
Karaboga D, Akay B (2011) A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput 11(3):3021–3031CrossRef
Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Foundations of fuzzy logic and soft computing, IFSA, pp 789–798
Kotonya G, Sommerville I (1998) Requirements engineering processes and techniques. Wiley, Hoboken
Kowalczyk M (2014) Big data and information processing in organizational decision processes. Bus Inf Syst Eng 6(5):267–278CrossRef
Lin C (2004) ROUGE: a package for automatic evaluation of summaries. In: Proceedings of the workshop in text summarization. ACL, pp 74–81
Lloret E, Romá-Ferri MT, Palomar M (2013) COMPENDIUM: a text summarization system for generating abstracts of research papers. Data Knowl Eng 88:164–175CrossRef
Lockshin LS, Spawton AL, Macintosh G (1997) Using product, brand and purchasing involvement for retail segmentation. J Retail Consum Serv 4(3):171–183CrossRef
Luhn HP (1958) The automatic creation of literature abstracts. IBM J Res Dev 2(2):159–165CrossRef
MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability. University of California Press, pp 287–297
Mani I, Maybury M (1999) Advances in automatic text summarization. MIT Press, Cambridge
Marcu D (1998) Improving summarization through rhetorical parsing tuning. In: Proceedings of the sixth workshop on very large corpora, pp 206–215
Mendoza M, Bonilla S, Noguera C, Cobos C, León E (2014) Extractive single-document summarization based on genetic operators and guided local search. Expert Syst Appl 41(9):4158–4169CrossRef
Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63(2):81–97CrossRef
Moens M (2007) Summarizing court decisions. Inf Process Manag 43(6):1748–1764CrossRef
Oberle D, Bhatti N, Brockmans S, Niemann M, Janiesch C (2009) Countering service information challenges in the internet of services. Bus Inf Syst Eng 1(5):370–390CrossRef
Okike C, Fernandes KJ (2012) Impact of information use architecture on load and usability. Inf Process Manag 48(5):995–1016CrossRef
O’Reilly CA (1983) The use of information in organizational decision making: a model and some propositions. Res Organ Behav 5:103–140
Orenga-Roglá S, Chalmeta R (2017) Methodology for the implementation of knowledge management systems. Bus Inf Syst Eng 2:1–19
Rackoff N, Wiseman C, Ulrich WA (1985) Information systems for competitive advantage: implementation of a planning process. MIS Q 9(4):285–294CrossRef
Radev DR (2004) MEAD – a platform for multidocument multilingual text summarization. In: Proceedings of LREC, Lisbon
Radev DR, Jing H, Budzikowska M (2000) Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. In: NAACL-ANLP 2000 workshop on automatic summarization, pp 21–30
Radev DR, Hovy E, McKeown K (2002) Introduction to the special issue on summarization. Comput Linguist 28(4):399–408CrossRef
Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst 89:14–46CrossRef
Reeve LH, Han H, Brooks AD (2007) The use of domain-specific concepts in biomedical text summarization. Inf Process Manag 43(6):1765–1776CrossRef
Sankarasubramaniam Y, Ramanathan K, Ghosh S (2014) Text summarization using Wikipedia. Inf Process Manag 50(3):443–461CrossRef
Simon HA (1996) The sciences of the artificial. MIT Press, Cambridge
Simon B (2010) A discussion on competency management systems from a design theory perspective. Bus Inf Syst Eng 2(6):337–346CrossRef
Storn R, Price K (1996) Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. University of California, Berkeley
Stroh F, Winter R, Wortmann F (2011) Method support of information requirements analysis for analytical information systems. Bus Inf Syst Eng 3(1):33–43CrossRef
Tallon PP, Ramirez RV, Short JE (2013–2014) The information artifact in IT governance: toward a theory of information governance. J Manag Inf Syst 30(3):141–147
Tseng YH, Lin CJ, Lin Y (2007) Text mining techniques for patent analysis. Inf Process Manag 43(5):1216–1247CrossRef
Wang YD, Forgionne G (2006) A decision-theoretic approach to the evaluation of information retrieval systems. Inf Process Manag 42(4):863–874CrossRef
Wang W, Li S, Li J, Li W, Wei F (2013) Exploring hypergraph-based semi-supervised ranking for query-oriented summarization. Inf Sci 237:271–286CrossRef
Wang N, Sun S, OuYang D (2016) Business process modeling abstraction based on semi-supervised clustering analysis. Bus Inf Syst Eng 1–18
Wright S, Pickton DW, Callow J (2002) Competitive intelligence in UK Firms: a typology. Mark Intell Plan 20(6):349–360CrossRef
Xu M, Ong V, Duan Y, Mathews B (2011) Intelligent agent systems for executive information scanning, filtering and interpretation: perceptions and challenges. Inf Process Manag 47(2):186–201CrossRef
Zaby C, Wilde KD (2017) Intelligent business processes in CRM. Bus Inf Syst Eng 1–16
Zhan J, Loh HT, Liu Y (2009) Gather customer concerns from online product reviews – a text summarization approach. Expert Syst Appl 36(2):2107–2115CrossRef
Metadata
Title
Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization
Authors
Swapnajit Chakraborti
Shubhamoy Dey
Publication date
16-10-2018
Publisher
Springer Fachmedien Wiesbaden
Published in
Business & Information Systems Engineering / Issue 3/2019
Print ISSN: 2363-7005
Electronic ISSN: 1867-0202
DOI
https://doi.org/10.1007/s12599-018-0562-0

Other articles of this Issue 3/2019

Business & Information Systems Engineering 3/2019 Go to the issue

State of the Art

Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms

Research Paper

Conceptual Framework for SDSS Development with an Application in the Retail Industry

Research Paper

Performance Analysis and Enhancement of Deep Convolutional Neural Network

Profile

Interview with Erich Vad on “Political and Security Aspects of Digitization”

Research Paper

An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem

Catchword

Augmented Analytics

Premium Partner

    Image Credits