Multi-document summarization using cluster-based link analysis
By Xiaojun Wan, Jianwu Yang. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008
334 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/747112c833fbd5e60825d1ed005230808dc5da33
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
Citations in the corpus
- Improving the Estimation of Word Importance for News Multi-Document Summarization (2014, methodology): “Graph methods have been successfully applied to weighting sentences for generic (Wan and Yang, 2008; Mihalcea and Tarau, 2004; Erkan and Radev, 2004) and query-focused summarization (Otterbacher et al., 2009).”
- Text Summarization Techniques: A Brief Survey (2017, methodology): “i.e. central value, positional value and first-sentence overlap) are used. Next, the final score of each sentence is computed and the selection of sentences is determined. For another related work, see [76]. 3.3 Latent Semantic Analysis Latent semantic analysis (LSA) which is introduced by [20], is an”
- Multi-document Summarization via Deep Learning Techniques: A Survey (2020, methodology): “Existing methods using traditional algorithms are based on term frequency-inverse document frequency (TF-IDF) [11, 131], clustering [52, 159], graphs [104, 158], and latent semantic analysis [8, 60].”
- System Combination for Multi-document Summarization (2015, background): “Wang and Li (2012) propose an unsupervised method to minimize the distance of the final ranking compared to the initial rankings.”
- Improving the Estimation of Word Importance for News Multi-Document Summarization - Extended Technical Report (2016, methodology): “Graph methods have been successfully applied to weighting sentences for generic (Wan and Yang, 2008; Mihalcea and Tarau, 2004; Erkan and Radev, 2004) and query-focused summarization (Otterbacher et al., 2009).”
- Cross-Language Document Summarization Based on Machine Translation Quality Prediction (2010, background): “Cluster-level information has been incorporated in the graph model to better evaluate sentences (Wan and Yang, 2008).”
- Evolutionary timeline summarization: a balanced optimization framework via iterative substitution (2011, background): “Cluster information has been incorporated in the graph model to better evaluate sentences [16].”