Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
Measuring importance and query relevance in topic-focused multi-document summarization
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
An Extractive Text Summarizer Based on Significant Words
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Query-based opinion summarization for legal blog entries
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Using non-lexical features to identify effective indexing terms for biomedical illustrations
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Graph-based multi-modality learning for topic-focused multi-document summarization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Detecting novelty in the context of progressive summarization
HLT-SRWS '10 Proceedings of the NAACL HLT 2010 Student Research Workshop
Towards a unified approach to simultaneous single-document and multi-document summarizations
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A study on position information in document summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Jointly learning to extract and compress
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Text summarisation in progress: a literature review
Artificial Intelligence Review
Improving the performance of the reinforcement learning model for answering complex questions
Proceedings of the 21st ACM international conference on Information and knowledge management
Towards content-level coherence with aspect-guided summarization
ACM Transactions on Speech and Language Processing (TSLP)
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We present a fast query-based multi-document summarizer called FastSum based solely on word-frequency features of clusters, documents and topics. Summary sentences are ranked by a regression SVM. The summarizer does not use any expensive NLP techniques such as parsing, tagging of names or even part of speech information. Still, the achieved accuracy is comparable to the best systems presented in recent academic competitions (i.e., Document Understanding Conference (DUC)). Because of a detailed feature analysis using Least Angle Regression (LARS), FastSum can rely on a minimal set of features leading to fast processing times: 1250 news documents in 60 seconds.