The budgeted maximum coverage problem
Information Processing Letters
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Multi-document summarization by sentence extraction
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Topic-driven multi-document summarization with encyclopedic knowledge and spreading activation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Comparative document summarization via discriminative sentence selection
Proceedings of the 18th ACM conference on Information and knowledge management
Multi-document summarization via budgeted maximization of submodular functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Multi-document summarization via submodularity
Applied Intelligence
Generating event storylines from microblogs
Proceedings of the 21st ACM international conference on Information and knowledge management
Ontology-enriched multi-document summarization in disaster management using submodular function
Information Sciences: an International Journal
iHR: an online recruiting system for Xiamen Talent Service Center
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
Multi-document summarization aims to distill the most representative information from a set of documents to generate a summary. Given a set of documents as input, most of existing multi-document summarization approaches utilize different sentence selection techniques to extract a set of sentences from the document set as the summary. The submodularity hidden in textual-unit similarity motivates us to incorporate this property into our solution to multi-document summarization tasks. In this poster, we propose a new principled and versatile framework for different multi-document summarization tasks using the submodular function [8].