Automatically summarising Web sites: is there a way around it?
Proceedings of the ninth international conference on Information and knowledge management
Summarizing Similarities and Differences Among Related Documents
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Construction of Ontology-Based User Model for Web Personalization
UM '07 Proceedings of the 11th international conference on User Modeling
Capturing User Interests by Both Exploitation and Exploration
UM '07 Proceedings of the 11th international conference on User Modeling
Aspect-Based Personalized Text Summarization
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Focused and aggregated search: a perspective from natural language generation
Information Retrieval
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The web has become a major source of information to learn about a topic. With the continuous growth of information and its high connectivity, it is hard to follow only the links that are relevant and not to get lost in hyperspace. Our aim is to support people who read documents in a highly connected information space, helping them remain on focus. Our contextually-aware in-browser text summarisation tool, IBES, does this by capturing users' current interests and providing users with contextualised summaries of linked documents, to help them decide whether the link is worth following.