The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Temporal summaries of new topics
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Term Weighting Approaches in Automatic Text Retrieval
Term Weighting Approaches in Automatic Text Retrieval
Web page summarization using dynamic content
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
FastSum: fast and accurate query-based multi-document summarization
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
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A Progressive summary helps a user to monitor changes in evolving news topics over a period of time. Detecting novel information is the essential part of progressive summarization that differentiates it from normal multi document summarization. In this work, we explore the possibility of detecting novelty at various stages of summarization. New scoring features, Re-ranking criterions and filtering strategies are proposed to identify "relevant novel" information. We compare these techniques using an automated evaluation framework ROUGE, and determine the best. Overall, our summarizer is able to perform on par with existing prime methods in progressive summarization.