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
Summarizing Similarities and Differences Among Related Documents
Information Retrieval
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
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
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
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Answer diversification for complex question answering on the web
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
| Hi-index | 0.00 |
This paper presents a novel method of generating extractive summaries for multiple documents. Given a cluster of documents, we firstly construct a graph where each vertex represents a sentence and edges are created according to the asymmetric relationship between sentences. Then we develop a method to measure the importance of a subset of vertices by adding a super-vertex into the original graph. The importance of such a super-vertex is quantified as super-centrality, a quantitative measure for the importance of a subset of vertices within the whole graph. Finally, we propose a heuristic algorithm to find the best summary. Our method is evaluated with extensive experiments. The comparative results show that the proposed method outperforms other methods on several datasets.