Applying regression models to query-focused multi-document summarization

By O. You, Wenjie Li, Sujian Li, Qin Lu. 2011

187 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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

1. Furu Wei
2. Mahak Gambhir
3. Vishal Gupta
4. Ting-Wei Liu
5. Bing Qin
6. Xiaojun Wan
7. Li Dong
8. Kai Hong

In Information Systems

1. Ting-Wei Liu
2. Bing Qin
3. Len Hamey
4. Mahmood Yousefi-Azar
5. Benno Stein
6. Xiaojun Wan
7. Li Dong
8. Krys J. Kochut

In Natural Language Processing

1. Furu Wei
2. Mahak Gambhir
3. Xiaojun Wan
4. Vishal Gupta
5. Li Dong
6. Ting-Wei Liu
7. Mirella Lapata
8. Mitchell P. Marcus

Citations in the corpus

• TL;DR: Mining Reddit to Learn Automatic Summarization (2017, methodology): “…systems, past research has focused on the use of various optimization methods—such as non-negative matrix factorization (Lee et al., 2009), support vector regression (Ouyang et al., 2011), and evolutionary algorithms (Alguliev et al., 2013)—to select salient sentences for an extractive summary.”
• Text Summarization Techniques: A Brief Survey (2017, methodology): “…of the frequent features used in summarization include the position of sentences in the document, sentence length, presence of uppercase words, similarity of the sentence to the document title, etc. Machine learning approaches have been widely used in summarization by [48, 78, 80], to name a few.”
• Multiple documents summarization based on evolutionary optimization algorithm (2013, methodology): “Some widely used classification techniques, e.g. neural network (Fattah & Ren, 2009) and support vector machine (Ouyang et al., 2011), are applied to achieve sentence classification.”
• Transforming Wikipedia Into Augmented Data for Query-Focused Summarization (2019, methodology): “, 2018) and supervised methods (Galley, 2006; Ouyang et al., 2011; Li et al., 2013; Cao et al., 2016; Ren et al., 2017) have been proposed for the purpose.”
• System Combination for Multi-document Summarization (2015, methodology): “Though prior work has extensively investigated features that are indicative of important words (Yih et al., 2007; Hong and Nenkova, 2014) or sentences (Litvak et al., 2010; Ouyang et al., 2011), little work has focused on designing global features defined over the summary.”
• Text summarization using unsupervised deep learning (2017, methodology): “Machine learning techniques have widely used for text summarization (Li et al., 2009; Ouyang et al., 2011; Conroy and O’leary, 2001; Corston-Oliver et al., 2004).”
• Recent automatic text summarization techniques: a survey (2016, methodology): “Summaries can also be of two types: generic or query-focused (Gong and Liu 2001; Dunlavy et al. 2007; Wan 2008; Ouyang et al. 2011).”
• Natural language processing: state of the art, current trends and challenges (2017, background): “Summaries can also be of two types: generic or query-focused (Gong and Liu 2001 [50]; Dunlavy et al. 2007 [37]; Wan 2008 [144]; Ouyang et al. 2011 [99]).”
• Cross-language document summarization via extraction and ranking of multiple summaries (2019, methodology): “centroid-based method [20], integer linear programming (ILP) [9], submodular function maximization [13–15], graph-based methods [5,24], and supervised learning-based methods [4,17,18,21].”