Multi-document Text Summarization for Competitor Intelligence: A Methodology

Chakraborti, Swapnajit; Dey, Subhrakanti

doi:10.1109/iscbi.2014.28

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…This approach is similar to ours in that it uses text-summarization as a tool for deceiving people into thinking a summarization is sharing one message, but this approach is not intended to actively persuade the target to mis-perceive the manipulated text. Another paper highlighted a possible application of text summarization to help summarize lots of text data about a rival company to build a concise competitor profile [5]. This paper uses text summarization to quickly perform a lot of research on a target than might otherwise be easily available.…”

Section: Discussion 71 Related Workmentioning

confidence: 99%

"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation

Jachim¹,

Sharevski²,

Pieroni³

2021

Preprint

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This paper presents Out-of-Context Summarizer, a tool that takes arbitrary public news articles out of context by summarizing them to coherently fit either a liberal-or conservative-leaning agenda. The Out-of-Context Summarizer also suggests hashtag keywords to bolster the polarization of the summary, in case one is inclined to take it to Twitter, Parler or other platforms for trolling. Out-of-Context Summarizer achieved 79% precision and 99% recall when summarizing COVID-19 articles, 93% precision and 93% recall when summarizing politically-centered articles, and 87% precision and 88% recall when taking liberally-biased articles out of context. Summarizing valid sources instead of synthesizing fake text, the Outof-Context Summarizer could fairly pass the "adversarial disclosure" test, but we didn't take this easy route in our paper. Instead, we used the Out-of-Context Summarizer to push the debate of potential misuse of automated text generation beyond the boilerplate text of responsible disclosure of adversarial language models. CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; • Computing methodologies → Information extraction;

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Section: Discussion 71 Related Workmentioning

confidence: 99%

"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation

Jachim¹,

Sharevski²,

Pieroni³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Although ATS has been used for summarizing from multiple sources, such as patents (Tseng et al 2007;Codina-Filbà et al 2017), biomedical text (Reeve et al 2007), research papers (Lloret et al 2013), IMF country reports (Ackermann et al 2006), product reviews (Zhan et al 2009;Hu et al 2017), court decisions (Moens 2007), product news (Chakraborti and Dey 2015), it has not been used specifically for extracting summaries from corpora created with the intention of gathering information on multiple aspects of a business organization's competitors. The conceptual framework proposed in Chakraborti and Dey (2014) and Chakraborti (2015) proposing ATS as a component for creating summaries from CI corpora lacks the support of any empirical analysis that shows the effectiveness of ATS for generating useful system summaries. The other work (Chakraborti and Dey 2015) focuses only on one aspect of CI, i.e., product news summarization.…”

Section: Automatic Text Summarizationmentioning

confidence: 99%

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

Chakraborti

Dey

2018

Bus Inf Syst Eng

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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.

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

Chakraborti

Dey

2016

2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)

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Multi-document Text Summarization for Competitor Intelligence: A Methodology

Cited by 4 publications

References 22 publications

"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation

"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation

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

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

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