Towards Multi-paper Summarization Using Reference Information
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Blind men and elephants: What do citation summaries tell us about a research article?
Journal of the American Society for Information Science and Technology
Scientific paper summarization using citation summary networks
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Automatic classification of citation function
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using citations to generate surveys of scientific paradigms
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
The ACL Anthology Network corpus
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Identifying non-explicit citing sentences for citation-based summarization
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Citation summarization through keyphrase extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Clairlib: a toolkit for natural language processing, information retrieval, and network analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Character-based kernels for novelistic plot structure
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Reference scope identification in citing sentences
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Context-enhanced citation sentiment detection
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Rediscovering ACL discoveries through the lens of ACL anthology network citing sentences
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
Identifying claimed knowledge updates in biomedical research articles
ACL '12 Proceedings of the Workshop on Detecting Structure in Scholarly Discourse
Detection of implicit citations for sentiment detection
ACL '12 Proceedings of the Workshop on Detecting Structure in Scholarly Discourse
Generating extractive summaries of scientific paradigms
Journal of Artificial Intelligence Research
The notion of diversity in graphical entity summarisation on semantic knowledge graphs
Journal of Intelligent Information Systems
Summarization of scientific documents by detecting common facts in citations
Future Generation Computer Systems
PSG: a two-layer graph model for document summarization
Frontiers of Computer Science: Selected Publications from Chinese Universities
The ACL anthology network corpus
Language Resources and Evaluation
Journal of Information Science
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In citation-based summarization, text written by several researchers is leveraged to identify the important aspects of a target paper. Previous work on this problem focused almost exclusively on its extraction aspect (i.e. selecting a representative set of citation sentences that highlight the contribution of the target paper). Meanwhile, the fluency of the produced summaries has been mostly ignored. For example, diversity, readability, cohesion, and ordering of the sentences included in the summary have not been thoroughly considered. This resulted in noisy and confusing summaries. In this work, we present an approach for producing readable and cohesive citation-based summaries. Our experiments show that the proposed approach outperforms several baselines in terms of both extraction quality and fluency.