A Study of Global Inference Algorithms in Multi-document Summarization

By Ryan T. McDonald. European Conference on Information Retrieval, 2007

392 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/daf1524c4be077a744f18fbcca4a9c992d7f583b

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In Artificial Intelligence

Citations in the corpus

Summarization Using Reinforcement Learning (2012, methodology): “Global inference algorithms for the extractive approach have been researched widely in recent years (Filatova and Hatzivassiloglou, 2004; McDonald, 2007; Takamura and Okumura, 2009) to consider whether the summary is “good” as a whole.”
Graph-based Neural Multi-Document Summarization (2017, methodology): “Then, they select summary-worthy sentences using a range of algorithms, such as graph centrality (Erkan and Radev, 2004), constraint optimization via Integer Linear Programming (McDonald, 2007; Gillick and Favre, 2009; Li et al., 2013), or Support Vector Regression (Li et al.”
A Scalable Global Model for Summarization (2009, methodology): “The model proposed by McDonald (2007) considers information and redundancy at the sentence level.”
Neural Document Summarization by Jointly Learning to Score and Select Sentences (2018, methodology): “McDonald (2007) treats sentence selection as an optimization problem under some constraints such as summary length.”
Learning Mixtures of Submodular Shells with Application to Document Summarization (2012, background): “Eqn (4) is known as the problem of submodular maximization subject to knapsack constraints [31] which NP-complete [35].”
Multi-document Summarization via Budgeted Maximization of Submodular Functions (2010, positive): “McDonald (2007) proposed to replace the greedy search of MMR with a globally optimal formulation, where the basic MMR framework can be expressed as a knapsack packing problem, and an integer linear program (ILP) solver can be used to maximize the resulting objective function.”
Improving the Estimation of Word Importance for News Multi-Document Summarization (2014, methodology): “Most optimization approaches, however, use TF*IDF or word probability in the input as word weights (McDonald, 2007; Shen and Li, 2010; Berg-Kirkpatrick et al., 2011).”
Exploring Content Models for Multi-Document Summarization (2009, methodology): “We attempted more complex inference procedures such as McDonald (2007), but these attempts only yielded negligible performance gains.”
Automatically Generating Wikipedia Articles: A Structure-Aware Approach (2009, methodology): “In prior research, ILP was used as a postprocessing step to remove redundancy and make other global decisions about parameters (McDonald, 2007; Marciniak and Strube, 2005; Clarke and La-pata, 2007).”
Multiple documents summarization based on evolutionary optimization algorithm (2013, background): “In (McDonald, 2007), the summarization task was defined as a global inference problem, which attempted to optimize three properties jointly, i.e., relevance, redundancy, and length.”
Toward Abstractive Summarization Using Semantic Representations (2018, methodology): “ form a summary. This is often combined with sentence compression, allowing more sentences to be included within a budget. ILPs and approximations have been used to encode compression and extraction (McDonald, 2007; Martins and Smith, 2009; Gillick and Favre, 2009; Berg-Kirkpatrick et al., 2011; Alm”
Long story short - Global unsupervised models for keyphrase based meeting summarization (2010, methodology): “Nevertheless, (McDonald, 2007) proposed to 355 3562 http://www.gnu.org/software/glpk/.”
Transforming Wikipedia Into Augmented Data for Query-Focused Summarization (2019, methodology): “Various unsupervised methods (Carbonell and Goldstein, 1998; Erkan and Radev, 2004; McDonald, 2007; Wan and Xiao, 2009; Feigenblat et al., 2017; Baumel et al., 2018) and supervised methods (Galley, 2006; Ouyang et al.”
Abstractive Document Summarization with a Graph-Based Attentional Neural Model (2017, background): “Notable early works include (Edmundson, 1969; Carbonell and Goldstein, 1998; McDonald, 2007).”
Improving the Estimation of Word Importance for News Multi-Document Summarization - Extended Technical Report (2016, methodology): “Most optimization approaches, however, use TF*IDF or word probability in the input as word weights (McDonald, 2007; Shen and Li, 2010; Berg-Kirkpatrick et al., 2011).”