Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
From single to multi-document summarization: a prototype system and its evaluation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Comments-oriented blog summarization by sentence extraction
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Comments-oriented document summarization: understanding documents with readers' feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Document-Based HITS Model for Multi-document Summarization
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Manifold-ranking based topic-focused multi-document summarization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Co-feedback ranking for query-focused summarization
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
DivRank: the interplay of prestige and diversity in information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A study on position information in document summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Comparative study of clustering techniques for short text documents
Proceedings of the 20th international conference companion on World wide web
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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With the popularity of Web 2.0, comments left by readers on web documents have drawn much attention. In this paper, we study the problem of comments-oriented document summarization, which aims to summarize a web document by considering not only its content but also the comments. Generally, most of the comments usually convey one or a few aspects of the document. Given a sentence set from both the web document and its corresponding comments to summarize, we can divide different sentences into different clusters (named "aspects") according to the content. It is challenging and interesting to summarize the web document based on these clusters. Motivated by this, we propose a novel model: MultiAspectCoRank, for comments-oriented document summarization. Firstly we rank all the sentences based on the multiple aspects obtained from the whole document, and then provide each ranking list as feedback to others until the top-N results of each ranking list are unchanged. We get the final result by integrating these different ranking lists together. Experimental results on a set of real-world blog data with manually labeled sentences show the promising performance of our approach.