Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Text summarization using a trainable summarizer and latent semantic analysis
Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
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
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
On the use of archetypes as benchmarks
Applied Stochastic Models in Business and Industry - Special issue on statistical methods in performance analysis
Automatic generic document summarization based on non-negative matrix factorization
Information Processing and Management: an International Journal
Biased LexRank: Passage retrieval using random walks with question-based priors
Information Processing and Management: an International Journal
Latent Dirichlet Allocation and Singular Value Decomposition Based Multi-document Summarization
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Making Archetypal Analysis Practical
Proceedings of the 31st DAGM Symposium on Pattern Recognition
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
A document-sensitive graph model for multi-document summarization
Knowledge and Information Systems
Weighted and robust archetypal analysis
Computational Statistics & Data Analysis
Archetypal analysis for machine learning and data mining
Neurocomputing
SumCR: A new subtopic-based extractive approach for text summarization
Knowledge and Information Systems
GenDocSum+MCLR: Generic document summarization based on maximum coverage and less redundancy
Expert Systems with Applications: An International Journal
CDDS: Constraint-driven document summarization models
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Most existing research on applying the matrix factorization approaches to query-focused multi-document summarization (Q-MDS) explores either soft/hard clustering or low rank approximation methods. We employ a different kind of matrix factorization method, namely weighted archetypal analysis (wAA) to Q-MDS. In query-focused summarization, given a graph representation of a set of sentences weighted by similarity to the given query, positively and/or negatively salient sentences are values on the weighted data set boundary. We choose to use wAA to compute these extreme values, archetypes, and hence to estimate the importance of sentences in target documents set. We investigate the impact of using the multi-element graph model for query focused summarization via wAA. We conducted experiments on the data of document understanding conference (DUC) 2005 and 2006. Experimental results evidence the improvement of the proposed approach over other closely related methods and many of state-of-the-art systems.