Cryptologia
IBM Systems Journal
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Hiding the Hidden: A software system for concealing ciphertext as innocuous text
ICICS '97 Proceedings of the First International Conference on Information and Communication Security
Plausible Deniability Using Automated Linguistic Stegonagraphy
InfraSec '02 Proceedings of the International Conference on Infrastructure Security
Hiding Data in the OSI Network Model
Proceedings of the First International Workshop on Information Hiding
A Practical and Effective Approach to Large-Scale Automated Linguistic Steganography
ISC '01 Proceedings of the 4th International Conference on Information Security
Increasing Robustness of LSB Audio Steganography Using a Novel Embedding Method
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Multidocument summarization: An added value to clustering in interactive retrieval
ACM Transactions on Information Systems (TOIS)
Automatic summarization of voicemail messages using lexical and prosodic features
ACM Transactions on Speech and Language Processing (TSLP)
A survey for multi-document summarization
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
Proceedings of the 2006 ACM symposium on Applied computing
Choosing the content of textual summaries of large time-series data sets
Natural Language Engineering
Investigating sentence weighting components for automatic summarisation
Information Processing and Management: an International Journal
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
Task-based evaluation of text summarization using Relevance Prediction
Information Processing and Management: an International Journal
Discriminative sentence compression with conditional random fields
Information Processing and Management: an International Journal
Using lexical chains for keyword extraction
Information Processing and Management: an International Journal
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Journal of Intelligent Information Systems
Graphstega: Graph Steganography Methodology
Journal of Digital Forensic Practice
Nostega: A Novel Noiseless Steganography Paradigm
Journal of Digital Forensic Practice
Listega: list-based steganography methodology
International Journal of Information Security
The automatic creation of literature abstracts
IBM Journal of Research and Development
Nostega: a novel noiseless steganography paradigm
Nostega: a novel noiseless steganography paradigm
Comprehensive linguistic steganography survey
International Journal of Information and Computer Security
Headstega: e-mail-headers-based steganography methodology
International Journal of Electronic Security and Digital Forensics
Translation-based steganography
IH'05 Proceedings of the 7th international conference on Information Hiding
Is image steganography natural?
IEEE Transactions on Image Processing
Jokestega: automatic joke generation-based steganography methodology
International Journal of Security and Networks
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The demand for reading while no one has time to read everything has fuelled the necessity for automatic summarisation systems in business, science, World Wide Web, education, news, etc. Thus, the popular use of summaries by a wide variety of people creates a high volume of traffic for accessing and generating summaries. Such huge traffic makes an adversary's job impractical to investigate all of them and allows communicating parties to establish a secure covert channel to transmit steganographic covers. This renders summaries an attractive steganographic carrier. Therefore, summarisation-based steganography methodology (Sumstega), presented in this paper, takes advantage of the automatic summarisation techniques to generate summary-cover. Sumstega neither hides data in a noise nor produces noise. Instead, Sumstega manipulates the parameters and factors of automatic summarisation techniques in order to embed data without noise, which retains adequate rooms for concealing data. The validation demonstrates the capability of achieving the steganographic goal.