TimedTextRank: adding the temporal dimension to multi-document summarization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Update summarization based on novel topic distribution
Proceedings of the 9th ACM symposium on Document engineering
Manifold-ranking based topic-focused multi-document summarization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Summarizing the differences in multilingual news
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Decayed DivRank: capturing relevance, diversity and prestige in information networks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Exploring hypergraph-based semi-supervised ranking for query-oriented summarization
Information Sciences: an International Journal
Diversified recommendation on graphs: pitfalls, measures, and algorithms
Proceedings of the 22nd international conference on World Wide Web
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Update summarization aims to create a summary over a topic-related multi-document dataset based on the assumption that the user has already read a set of earlier documents of the same topic. Beyond the problems (i.e., topic relevance, salience, and diversity in extracted information) tackled by topic-focused multi-document summarization, the update summarization must address the novelty problem as well. In this paper, we propose a novel extractive approach based on manifold ranking with sink points for update summarization. Specifically, our approach leverages a manifold ranking process over the sentence manifold to find topic relevant and salient sentences. More important, by introducing the sink points into sentence manifold, the ranking process can further capture the novelty and diversity based on the intrinsic sentence manifold. Therefore, we are able to address the four challenging problems above for update summarization in a unified way. Experiments on benchmarks of TAC are performed and the evaluation results show that our approach can achieve comparative performance to the existing best performing systems in TAC tasks.