Multi-document Summarization via Budgeted Maximization of Submodular Functions

By Hui-Ching Lin, J. Bilmes. 2010

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, neutral): “We also compare against the three highestscoring systems that actually competed in the DUC 2004 competition — peers 65, 104, and 35 — as well as the submodular graph-based approach recently described by Lin and Bilmes [94], which we refer to as submod1, and the improved submodular learning approach ”
Guiding Extractive Summarization with Question-Answering Rewards (2019, background): “…and optionally deleting unimportant sentence constituents to form a summary (Knight and Marcu, 2002; Radev et al., 2004; Zajic et al., 2007; Martins and Smith, 2009; Gillick and Favre, 2009; Lin and Bilmes, 2010; Wang et al., 2013; Li et al., 2013, 2014; Hong et al., 2014; Yogatama et al., 2015).”
Neural Latent Extractive Document Summarization (2018, neutral): “The ﬁnal score R ( C , H ) is the weighted sum of the two: Our use of the terms “precision” and “recall” is reminiscent of relevance and coverage in other summarization work (Carbonell and Goldstein, 1998; Lin and Bilmes, 2010; See et al., 2017).”
A Class of Submodular Functions for Document Summarization (2011, methodology): “The redundancy penalty usually violates the monotonicity of the objective functions (Carbonell and Goldstein, 1998; Lin and Bilmes, 2010).”
Toward Abstractive Summarization Using Semantic Representations (2018, methodology): “and Smith, 2009; Gillick and Favre, 2009; Berg-Kirkpatrick et al., 2011; Almeida and Martins, 2013; Li et al., 2014). Other decoding approaches have included a greedy method exploiting submodularity (Lin and Bilmes, 2010), document reconstruction (He et al., 2012), and graph cuts (Qian and Liu, 2013”
Some Submodular Data-Poisoning Attacks on Machine Learners (2015, methodology): “Following results in submodular maximization [11], the same algorithm used in ﬂipping-label attack can constructs the attack set S (cid:48) with a 1 − 1 √ e guarantee.”
Near-Optimal MAP Inference for Determinantal Point Processes (2012, background): “Informative subset selection problems arise in many applications where a small number of items must be chosen to represent or cover a much larger set; for instance, text summarization [1, 2], document and image search [3, 4, 5], sensor placement [6], viral marketing [7], and many others.”
Recent automatic text summarization techniques: a survey (2016, methodology): “2012) This is a supervised learning approach that is applicable to all sub-modular scoring functions ranging from pair-wise similarity models to coverage based approaches BudSub (Lin and Bilmes 2010) This is an unsupervised summarization approach that maximizes sub-modular functions with a constrain”
Phrase-based Compressive Cross-Language Summarization (2015, methodology): “Many studies have formalized text summarization tasks as submodular maximization problems (Lin and Bilmes, 2010; Lin and Bilmes, 2011; Morita et al., 2013).”
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints (2013, methodology): “We remark that we can also use a simpler version of the greedy iteration at every iteration [21, 17] and we obtain a guarantee of (1 + , 1/2(1− e−1)).”
Submodular Maximization with Cardinality Constraints (2014, methodology): “In machine learning, maximization of submodular functions has been used for document summarization [41] [42], sensor placement [36] [38] [35], and information gathering [37] (consult the references therein for additional applications in AI).”
Online Submodular Maximization with Preemption (2015, methodology): “In machine learning, maximization of submodular functions has been used for document summarization [46, 47], sensor placement [41, 42, 44], and information gathering [43].”
Learning Mixtures of Submodular Shells with Application to Document Summarization (2012, methodology): “In practice, moreover, as the approximation factor of the greedy algorithm on submodular maximization is usually very close to 1 [27], one could expect very little additional risk when using Algorithm 1 with approximate inference on learning submodular shell mixtures.”
Learning Determinantal Point Processes (2011, methodology): “Following previous work, we experiment with data from the Document Understanding Conference (DUC) 2003 and 2004 multi-document summarization task (Task 2) (Lin and Bilmes, 2010; Dang, 2005).”