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
Sub-event based multi-document summarization
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
Modeling local coherence: an entity-based approach
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Language independent extractive summarization
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Deriving event relevance from the ontology constructed with formal concept analysis
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Extractive spoken document summarization for information retrieval
Pattern Recognition Letters
Sentence Ordering for Coherent Multi-document Summary Generation
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
Query-Oriented Summarization Based on Neighborhood Graph Model
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Extractive summarization based on event term clustering
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Extractive summarization using supervised and semi-supervised learning
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A parse-and-trim approach with information significance for Chinese sentence compression
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Non-textual event summarization by applying machine learning to template-based language generation
UCNLG+Sum '09 Proceedings of the 2009 Workshop on Language Generation and Summarisation
Detection of Difference between News Articles on the Same Topic Based on Sequential Comparison
Proceedings of the 2010 conference on Information Modelling and Knowledge Bases XXI
Sentence-level event classification in unstructured texts
Information Retrieval
A cluster-sensitive graph model for query-oriented multi-document summarization
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Extracting 5W1H event semantic elements from Chinese online news
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Identification of rhetorical roles for segmentation and summarization of a legal judgment
Artificial Intelligence and Law
Summarizing non-textual events with a 'briefing' focus
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Building document graphs for multiple news articles summarization: an event-based approach
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
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Event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences describe. Each event has its own internal structure, and meanwhile often relates to other events semantically, temporally, spatially, causally or conditionally. In this paper, we define an event as one or more event terms along with the named entities associated, and present a novel approach to derive intra- and inter- event relevance using the information of internal association, semantic relatedness, distributional similarity and named entity clustering. We then apply PageRank ranking algorithm to estimate the significance of an event for inclusion in a summary from the event relevance derived. Experiments on the DUC 2001 test data shows that the relevance of the named entities involved in events achieves better result when their relevance is derived from the event terms they associate. It also reveals that the topic-specific relevance from documents themselves outperforms the semantic relevance from a general purpose knowledge base like Word-Net.