The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries

By Jaime G. Carbonell, Jade Goldstein-Stewart. 1998

Subfields: Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Fine-tune BERT for Extractive Summarization (2019, methodology): “This is similar to the Maximal Marginal Relevance (MMR) (Carbonell and Goldstein, 1998) but much simpler.”
A Diversity-Promoting Objective Function for Neural Conversation Models (2015, background): “Carbonell and Goldstein (1998) and Gimpel et al. (2013) produce multiple outputs that are mutually diverse, either non-redundant summary sentences or N-best lists.”
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (2019, methodology): “Traditional non-neural approaches to multi-document summarization have been both extractive (Carbonell and Goldstein, 1998; Radev et al., 2000; Erkan and Radev, 2004; Mihalcea and Ta-rau, 2004; Haghighi and Vanderwende, 2009) as well as abstractive (McKeown and Radev, 1995; Radev and McKeown, 1998;…”
Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints (2016, background): “While multi-document summarization is wellstudied in the NLP literature (Carbonell and Goldstein, 1998; Gillick and Favre, 2009; Lin and Bilmes, 2011; Nenkova and McKeown, 2011), single-document summarization (McKeown et al.”
Neural Document Summarization by Jointly Learning to Score and Select Sentences (2018, methodology): “In recent years, extractive methods for summarization have proven effective in many systems (Carbonell and Goldstein, 1998; Mihalcea and Tarau, 2004; McDonald, 2007; Cao et al., 2015a).”
Abstractive Document Summarization with a Graph-Based Attentional Neural Model (2017, background): “Notable early works include (Edmundson, 1969; Carbonell and Goldstein, 1998; McDonald, 2007).”
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization (2021, methodology): “Earlier extractive MDS work have used various approaches including maximum marginal relevance (MMR) to reduce redundancy (Carbonell and Goldstein 1998), clustering based on topic detection (Radev et al. 2004), graph-based (Erkan and Radev 2004) or hierarchical LDA-style models (Haghighi and…”
Fairness of Exposure in Rankings (2018, background): “Under extrinsic diversity [24], the utility measure accounts for uncertainty and diminishing returns from multiple relevant results [7, 25].”
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations (2018, methodology): “One of the most popular result set diversication methods is Maximal Marginal Relevance (MMR) [5].”
Objective Function Learning to Match Human Judgements for Optimization-Based Summarization (2018, background): “, 2014) in an ad-hoc fashion (Carbonell and Goldstein, 1998; Ren et al., 2016).”