Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Building natural language generation systems
Building natural language generation systems
Creating and evaluating multi-document sentence extract summaries
Proceedings of the ninth international conference on Information and knowledge management
Summarizing Similarities and Differences Among Related Documents
Information Retrieval
Temporal summaries of new topics
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
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
Towards CST-enhanced summarization
Eighteenth national conference on Artificial intelligence
Generating natural language summaries from multiple on-line sources: language reuse and regeneration
Generating natural language summaries from multiple on-line sources: language reuse and regeneration
On-line new event detection, clustering, and tracking (information retrieval, internet)
On-line new event detection, clustering, and tracking (information retrieval, internet)
Learning cross-document structural relationships using boosting
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
Building applied natural language generation systems
Natural Language Engineering
The rhetorical parsing of natural language texts
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Ontologies: How can They be Built?
Knowledge and Information Systems
Information fusion in the context of multi-document summarization
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A common theory of information fusion from multiple text sources step one: cross-document structure
SIGDIAL '00 Proceedings of the 1st SIGdial workshop on Discourse and dialogue - Volume 10
Summarization from medical documents: a survey
Artificial Intelligence in Medicine
A Greek morphological lexicon and its exploitation by natural language processing applications
PCI'01 Proceedings of the 8th Panhellenic conference on Informatics
CACLA '09 Proceedings of the EACL 2009 Workshop on Cognitive Aspects of Computational Language Acquisition
Sumstega: summarisation-based steganography methodology
International Journal of Information and Computer Security
Semantic search in the World News domain using automatically extracted metadata files
Knowledge-Based Systems
| Hi-index | 0.00 |
In this paper we present a fresh look at the problem of summarizing evolving events from multiple sources. After a discussion concerning the nature of evolving events we introduce a distinction between linearly and non-linearly evolving events. We present then a general methodology for the automatic creation of summaries from evolving events. At its heart lie the notions of Synchronic and Diachronic cross-document Relations (SDRs), whose aim is the identification of similarities and differences between sources, from a synchronical and diachronical perspective. SDRs do not connect documents or textual elements found therein, but structures one might call messages. Applying this methodology will yield a set of messages and relations, SDRs, connecting them, that is a graph which we call grid. We will show how such a grid can be considered as the starting point of a Natural Language Generation System. The methodology is evaluated in two case-studies, one for linearly evolving events (descriptions of football matches) and another one for non-linearly evolving events (terrorist incidents involving hostages). In both cases we evaluate the results produced by our computational systems.